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style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p class="label">Followers</p><p class="data">166</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">8</p></div></a><a><div class="stat-container js-profile-coauthors" data-broccoli-component="user-info.coauthors-count" data-click-track="profile-expand-user-info-coauthors"><p class="label">Co-authors</p><p class="data">7</p></div></a><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="user-bio-container"><div class="profile-bio fake-truncate js-profile-about" style="margin: 0px;">Professor of Operations Research and Director of Postgraduate Research at UNSW Csnberra<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="31714480" href="https://www.academia.edu/Documents/in/Production_and_Inventory_planning_and_control_systems"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://unsw.academia.edu/RuhulSarker","location":"/RuhulSarker","scheme":"https","host":"unsw.academia.edu","port":null,"pathname":"/RuhulSarker","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" 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data-work-id="86115013"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86115013/Evolutionary_Approaches_for_Project_Portfolio_Optimization_An_Overview"><img alt="Research paper thumbnail of Evolutionary Approaches for Project Portfolio Optimization: An Overview" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86115013/Evolutionary_Approaches_for_Project_Portfolio_Optimization_An_Overview">Evolutionary Approaches for Project Portfolio Optimization: An Overview</a></div><div class="wp-workCard_item"><span>Adaptation, Learning, and Optimization</span><span>, 2021</span></div><div class="wp-workCard_item 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})(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86115013]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86115013,"title":"Evolutionary Approaches for Project Portfolio Optimization: An Overview","translated_title":"","metadata":{"publisher":"Springer International Publishing","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Adaptation, Learning, and 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href="https://www.academia.edu/86115011/Adaptive_Sorting_Based_Evolutionary_Algorithm_for_Many_Objective_Optimization">Adaptive Sorting-Based Evolutionary Algorithm for Many-Objective Optimization</a></div><div class="wp-workCard_item"><span>IEEE Transactions on Evolutionary Computation</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cce66ffba7d177e850cdc24b5089ea0c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641479,"asset_id":86115011,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641479/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span 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_.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cce66ffba7d177e850cdc24b5089ea0c" } } $('.js-work-strip[data-work-id=86115011]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86115011,"title":"Adaptive Sorting-Based Evolutionary Algorithm for Many-Objective Optimization","translated_title":"","metadata":{"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","grobid_abstract":"Evolutionary algorithms have shown their promise in coping with many-objective optimization problems. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86115008"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86115008/Co_evolutionary_approach_for_strategic_bidding_in_competitive_electricity_markets"><img alt="Research paper thumbnail of Co-evolutionary approach for strategic bidding in competitive electricity markets" class="work-thumbnail" src="https://attachments.academia-assets.com/90641476/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86115008/Co_evolutionary_approach_for_strategic_bidding_in_competitive_electricity_markets">Co-evolutionary approach for strategic bidding in competitive electricity markets</a></div><div class="wp-workCard_item"><span>Applied Soft Computing</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c6cad2fb5c4cf4b8aaced1e24e20149f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641476,"asset_id":86115008,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641476/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86115008"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86115008"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86115008; 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In this paper, it is formulated as a bi-level optimization problem in which, in the lower level, the community's social welfare is maximized by solving a power flow problem while, in the upper level, the profits of individual bidders are maximized. In this bidders' game, instead of using a set of discrete strategies as is usual, we consider continuous functions as strategies. To solve the upper-level problem, two co-evolutionary approaches are proposed and, for the lower level, an interior point algorithm is applied. Three IEEE benchmark problems in four different scenarios are solved and their results compared with those obtained from two conventional approaches and the literature which indicate that the proposed approaches have some merit regarding quality and efficiency.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Applied Soft Computing","grobid_abstract_attachment_id":90641476},"translated_abstract":null,"internal_url":"https://www.academia.edu/86115008/Co_evolutionary_approach_for_strategic_bidding_in_competitive_electricity_markets","translated_internal_url":"","created_at":"2022-09-04T01:54:58.247-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641476,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641476/thumbnails/1.jpg","file_name":"109681.pdf","download_url":"https://www.academia.edu/attachments/90641476/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Co_evolutionary_approach_for_strategic_b.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641476/109681-libre.pdf?1662290985=\u0026response-content-disposition=attachment%3B+filename%3DCo_evolutionary_approach_for_strategic_b.pdf\u0026Expires=1732705642\u0026Signature=T8tGC5Y66RDfPExELMnWwcWigqO0G9G8zBGXXzAXfeZQePqwUa8zARRK~ArschKWHndOcJHvbyaREjPrPN~IDwue3uZ3sAIHY7z4zdDMPLX-8AYdK752Xyg91TnOJ4BrzpCg0iBrXFnmLumjWEv5W48uTXky8SwmdvDhSJgvZaUC8ZpEXzJSaFnjjXWdK-1cKtpqaCGFEOHEYMyyIwu8794IlZPjN4tNVQFFRWZd8wVStr9SYyGFDNxj-iC~kRxSejD6WW45DF1TmJXG0SG5eWGIstiYrNjnTm3eZh5d0hqnKzaioTjmRWibYQOCo8fISUla2TFffVZNSmEHddHVfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Co_evolutionary_approach_for_strategic_bidding_in_competitive_electricity_markets","translated_slug":"","page_count":3,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641476,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641476/thumbnails/1.jpg","file_name":"109681.pdf","download_url":"https://www.academia.edu/attachments/90641476/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Co_evolutionary_approach_for_strategic_b.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641476/109681-libre.pdf?1662290985=\u0026response-content-disposition=attachment%3B+filename%3DCo_evolutionary_approach_for_strategic_b.pdf\u0026Expires=1732705642\u0026Signature=T8tGC5Y66RDfPExELMnWwcWigqO0G9G8zBGXXzAXfeZQePqwUa8zARRK~ArschKWHndOcJHvbyaREjPrPN~IDwue3uZ3sAIHY7z4zdDMPLX-8AYdK752Xyg91TnOJ4BrzpCg0iBrXFnmLumjWEv5W48uTXky8SwmdvDhSJgvZaUC8ZpEXzJSaFnjjXWdK-1cKtpqaCGFEOHEYMyyIwu8794IlZPjN4tNVQFFRWZd8wVStr9SYyGFDNxj-iC~kRxSejD6WW45DF1TmJXG0SG5eWGIstiYrNjnTm3eZh5d0hqnKzaioTjmRWibYQOCo8fISUla2TFffVZNSmEHddHVfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":38644,"name":"Bidding","url":"https://www.academia.edu/Documents/in/Bidding"}],"urls":[{"id":23593463,"url":"https://api.elsevier.com/content/article/PII:S1568494616306196?httpAccept=text/plain"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86115005"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86115005/Scenario_based_multi_period_program_optimization_for_capability_based_planning_using_evolutionary_algorithms"><img alt="Research paper thumbnail of Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/90641473/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86115005/Scenario_based_multi_period_program_optimization_for_capability_based_planning_using_evolutionary_algorithms">Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms</a></div><div class="wp-workCard_item"><span>Applied Soft Computing</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3b8ce520d99ad54c42af109b1e035f99" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641473,"asset_id":86115005,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641473/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86115005"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86115005"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86115005; 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Capability programming is an integral part of CBP which requires selecting a portfolio of capability projects for execution, referred as a capability program, such that the overall strategic risk facing the planning organization across a number of projected future operating scenarios is minimized while maintaining the most economical choice. It is a challenging optimization problem that requires handling a number of dynamic constraints and objectives that vary throughout the entire planning horizon. An optimizing simulation approach is presented in this paper that combines an evolutionary multi-objective optimization algorithm with a reinforcement learning technique to generate capability programs which optimize strategic risks and program costs across multiple planning scenarios as well as over a rolling planning horizon. The role of the optimization algorithm in this approach is to search for the non-dominated capability programs at each decision point by minimizing the strategic risks associated with individual capability projects across a number of planning scenarios as well as the total cost of the program. The reinforcement learning algorithm, on the other hand, searches horizontally within the set of non-dominated programs to minimize capability risks and costs over the entire planning horizon. The methodology is evaluated on a test problem generated based on the data distributions in an Australian Defence Capability Plan and the performance is compared with two myopic heuristic methods.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Applied Soft Computing","grobid_abstract_attachment_id":90641473},"translated_abstract":null,"internal_url":"https://www.academia.edu/86115005/Scenario_based_multi_period_program_optimization_for_capability_based_planning_using_evolutionary_algorithms","translated_internal_url":"","created_at":"2022-09-04T01:54:56.774-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641473,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641473/thumbnails/1.jpg","file_name":"111183.pdf","download_url":"https://www.academia.edu/attachments/90641473/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Scenario_based_multi_period_program_opti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641473/111183-libre.pdf?1662290984=\u0026response-content-disposition=attachment%3B+filename%3DScenario_based_multi_period_program_opti.pdf\u0026Expires=1732705642\u0026Signature=BuEZZ2spyx0Ois9yKaVd9~bTtD1HTAiob6AiiWMYPuzEEFtk~m-xpZlunggIdKkT3Zmili1~Id7F86zJSNbMoeCWX-giofbkPE6HtjW76PUQIwZjhVZLZPMSDeui0Jd7cJqbNCYNhXC26tcrpR1Khn5aAKbr7ReXTUE2LuK7JpRgyUfAUXTDbn0R4A6TGAderOCzzGwkgJSZRRwiw1Jhz6q4n3HJ5GjjRoJou5JGYn46WmnYIsLpvBlmPYQrQmIOXM0RIQCNYAm5TF9Y5FeJamyiH8g9Q3K5oIDV4B4Wxri4cGwPuOLoyIhfeKpai6SigDXBZpDPwHpj0c~RcGh1Kg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Scenario_based_multi_period_program_optimization_for_capability_based_planning_using_evolutionary_algorithms","translated_slug":"","page_count":3,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641473,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641473/thumbnails/1.jpg","file_name":"111183.pdf","download_url":"https://www.academia.edu/attachments/90641473/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Scenario_based_multi_period_program_opti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641473/111183-libre.pdf?1662290984=\u0026response-content-disposition=attachment%3B+filename%3DScenario_based_multi_period_program_opti.pdf\u0026Expires=1732705642\u0026Signature=BuEZZ2spyx0Ois9yKaVd9~bTtD1HTAiob6AiiWMYPuzEEFtk~m-xpZlunggIdKkT3Zmili1~Id7F86zJSNbMoeCWX-giofbkPE6HtjW76PUQIwZjhVZLZPMSDeui0Jd7cJqbNCYNhXC26tcrpR1Khn5aAKbr7ReXTUE2LuK7JpRgyUfAUXTDbn0R4A6TGAderOCzzGwkgJSZRRwiw1Jhz6q4n3HJ5GjjRoJou5JGYn46WmnYIsLpvBlmPYQrQmIOXM0RIQCNYAm5TF9Y5FeJamyiH8g9Q3K5oIDV4B4Wxri4cGwPuOLoyIhfeKpai6SigDXBZpDPwHpj0c~RcGh1Kg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":23593461,"url":"https://api.elsevier.com/content/article/PII:S1568494616303349?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86115001"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/86115001/Investigating_Multi_Operator_Differential_Evolution_for_Feature_Selection"><img alt="Research paper thumbnail of Investigating Multi-Operator Differential Evolution for Feature Selection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/86115001/Investigating_Multi_Operator_Differential_Evolution_for_Feature_Selection">Investigating Multi-Operator Differential Evolution for Feature Selection</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Performance issues when dealing with a large number of features are well-known for classification...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. The algorithm partitions the initial population into a number of sub-populations evolving using a pool of distinct mutation strategies. Periodically, the sub-populations exchange information to enhance their diversity. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. Two classifiers, namely decision trees and k-nearest neighborhood, are used to evaluate the generated subsets of features. Experimental analysis has been conducted on several real data sets using a 10-fold cross validation. The analysis shows that the proposed algorithm successfully determines efficient feature subsets, which can improve the classification accuracy of the classifiers under consideration. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set, where the proposed method can effectively remove irrelevant features from the data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86115001"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86115001"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86115001; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86115001]").text(description); $(".js-view-count[data-work-id=86115001]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86115001; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86115001']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86115001, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86115001]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86115001,"title":"Investigating Multi-Operator Differential Evolution for Feature Selection","translated_title":"","metadata":{"abstract":"Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. The algorithm partitions the initial population into a number of sub-populations evolving using a pool of distinct mutation strategies. Periodically, the sub-populations exchange information to enhance their diversity. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. Two classifiers, namely decision trees and k-nearest neighborhood, are used to evaluate the generated subsets of features. Experimental analysis has been conducted on several real data sets using a 10-fold cross validation. The analysis shows that the proposed algorithm successfully determines efficient feature subsets, which can improve the classification accuracy of the classifiers under consideration. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set, where the proposed method can effectively remove irrelevant features from the data.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Lecture Notes in Computer Science"},"translated_abstract":"Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. The algorithm partitions the initial population into a number of sub-populations evolving using a pool of distinct mutation strategies. Periodically, the sub-populations exchange information to enhance their diversity. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. Two classifiers, namely decision trees and k-nearest neighborhood, are used to evaluate the generated subsets of features. Experimental analysis has been conducted on several real data sets using a 10-fold cross validation. The analysis shows that the proposed algorithm successfully determines efficient feature subsets, which can improve the classification accuracy of the classifiers under consideration. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set, where the proposed method can effectively remove irrelevant features from the data.","internal_url":"https://www.academia.edu/86115001/Investigating_Multi_Operator_Differential_Evolution_for_Feature_Selection","translated_internal_url":"","created_at":"2022-09-04T01:54:55.267-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Investigating_Multi_Operator_Differential_Evolution_for_Feature_Selection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution"},{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection"},{"id":115676,"name":"Cyber Security","url":"https://www.academia.edu/Documents/in/Cyber_Security"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114998"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86114998/Solving_an_economic_and_environmental_dispatch_problem_using_evolutionary_algorithm"><img alt="Research paper thumbnail of Solving an economic and environmental dispatch problem using evolutionary algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/90641468/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86114998/Solving_an_economic_and_environmental_dispatch_problem_using_evolutionary_algorithm">Solving an economic and environmental dispatch problem using evolutionary algorithm</a></div><div class="wp-workCard_item"><span>2014 IEEE International Conference on Industrial Engineering and Engineering Management</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="744cff46512e09cef0969fd8df415d77" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641468,"asset_id":86114998,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641468/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114998"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114998"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114998; 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In this paper, we consider a power system with two types of generators, thermal and hydro. The characteristics of these generators vary with respect to the cost, emission to the environment, input source, capacity limit, and technological constraints. The mathematical model considering two objectives, such as minimization of the operating cost and minimization of total emissions, for a hydrothermal system is discussed. A solution approach has been proposed, based on evolutionary computation concept, for solving a benchmark problem for both single and bi-objective version of the problem. In the approach, an initial population of solutions is generated based on a heuristic and the population is then evolved using two well-known evolutionary search algorithms. The solutions of our approaches are compared with another approach from the literature. 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Numerical results, statistical analysis and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed approach. 2 1 j (4) a {0, 1} jt (5) The objective function is the minimization of makespan, C max (Eq. (1)). Eq. (2) ensures that an activity can be executed only once. Eq. (3) ensures that each activity j cannot be started unless all its predecessors have been completed. Eq. (4) ensures that an activity can be started when its required renewable resources (such as workforce, machines, tools or equipment) are available. Over the years, exact techniques such as branch and bound [4-6], branch and cut [7], and the event based approach [8] have been proposed for the optimal solution of RCPSPs. 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In the first part of the paper, a mathematical model was developed for the optimization of replenishment and production decisions for each node after the occurrence of a transportation disruption. In addition, an experiment was conducted to study the effects of disruptions to the system using predefined scenarios, where the supplier’s prioritization of disruption mitigation strategies was explored. Various disruption scenarios were predefined by combining different disruption types and locations as well as different combinations of suppliers. It will be shown that the solution to the transportation disruption was more sensitive to the lot size when the lost sales cost was large. 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However, when the lost sales cost was low, the sensitivity to the lot size decreased, and the setup cost and inventory holding co...","publisher":"ICIS 2016","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"This paper presents the study of a real-time procurement and production mechanism for a three-stage supply chain system with multiple suppliers, subject to unexpected disruptions. In the first part of the paper, a mathematical model was developed for the optimization of replenishment and production decisions for each node after the occurrence of a transportation disruption. In addition, an experiment was conducted to study the effects of disruptions to the system using predefined scenarios, where the supplier’s prioritization of disruption mitigation strategies was explored. Various disruption scenarios were predefined by combining different disruption types and locations as well as different combinations of suppliers. It will be shown that the solution to the transportation disruption was more sensitive to the lot size when the lost sales cost was large. However, when the lost sales cost was low, the sensitivity to the lot size decreased, and the setup cost and inventory holding co...","internal_url":"https://www.academia.edu/86114978/Real_Time_disruption_management_in_a_coordinated_supply_chain_system_with_multiple_suppliers","translated_internal_url":"","created_at":"2022-09-04T01:54:11.944-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641450,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641450/thumbnails/1.jpg","file_name":"ILS2016_FB04_2.pdf","download_url":"https://www.academia.edu/attachments/90641450/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Real_Time_disruption_management_in_a_coo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641450/ILS2016_FB04_2-libre.pdf?1662290993=\u0026response-content-disposition=attachment%3B+filename%3DReal_Time_disruption_management_in_a_coo.pdf\u0026Expires=1732705643\u0026Signature=KWuDp7ZHXvxzSZ1kY2UkosyFQBbwr938gleaqS73Z9gZSIQS8Zbz30Re6qRqLnvkyO4mdT6I6sZttCHLSbNuBYRIhQt3Liotd3-xZaHgVqN~6uz7N6DRT22zT6o0M-NP5rJzMchynWQV3sQP6tQ-Pw3MC5Mes8zwjhi4xmj88PLIngv2D~xbcEFqArJ-6987HWcR3KH4foydcyJleyhZQOSjs1-jnQfGLf2c2ya~vr7TDvFJ5KekHdnJlCuUVEHFzamiZ6MUTl2wX0YeKNzFcLn~MvD9eu9Ob6YBUApSfNPeU819YERgMD2AbDHs~VFHmlW9tZIVfZF268kpCvyeHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Real_Time_disruption_management_in_a_coordinated_supply_chain_system_with_multiple_suppliers","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641450,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641450/thumbnails/1.jpg","file_name":"ILS2016_FB04_2.pdf","download_url":"https://www.academia.edu/attachments/90641450/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Real_Time_disruption_management_in_a_coo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641450/ILS2016_FB04_2-libre.pdf?1662290993=\u0026response-content-disposition=attachment%3B+filename%3DReal_Time_disruption_management_in_a_coo.pdf\u0026Expires=1732705643\u0026Signature=KWuDp7ZHXvxzSZ1kY2UkosyFQBbwr938gleaqS73Z9gZSIQS8Zbz30Re6qRqLnvkyO4mdT6I6sZttCHLSbNuBYRIhQt3Liotd3-xZaHgVqN~6uz7N6DRT22zT6o0M-NP5rJzMchynWQV3sQP6tQ-Pw3MC5Mes8zwjhi4xmj88PLIngv2D~xbcEFqArJ-6987HWcR3KH4foydcyJleyhZQOSjs1-jnQfGLf2c2ya~vr7TDvFJ5KekHdnJlCuUVEHFzamiZ6MUTl2wX0YeKNzFcLn~MvD9eu9Ob6YBUApSfNPeU819YERgMD2AbDHs~VFHmlW9tZIVfZF268kpCvyeHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":26,"name":"Business","url":"https://www.academia.edu/Documents/in/Business"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1335,"name":"Supply Chain Management","url":"https://www.academia.edu/Documents/in/Supply_Chain_Management"},{"id":24699,"name":"Supply Chain","url":"https://www.academia.edu/Documents/in/Supply_Chain"}],"urls":[{"id":23593448,"url":"http://ils2016conference.com/wp-content/uploads/2015/03/ILS2016_FB04_2.pdf"}]}, dispatcherData: dispatcherData }); 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Manually designing a CNN is a time-consuming process in regards to the various layers that it can have, and the variety of parameters that must be set up. Increasing the complexity of the network structure by employing various types of connections makes designing a network even more challenging. Evolutionary computation as an optimisation technique can be applied to arrange the CNN layers and/or initiate its parameters automatically or semi-automatically. Dense network and Residual network are two popular network structures that were introduced to facilitate the training of deep networks. In this paper, leveraging the potentials of Dense and Residual blocks, and using the capability of evolutionary computation, we propose an automatic evolutionary model to detect an optimum and accurate network structure and its parameters for medical image segmentation. The proposed evolutionary DenseRes model is employed for segmentation of six publicly available MRI and CT medical datasets. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114976"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86114976/Managing_supply_disruption_in_a_three_tier_supply_chain_with_multiple_suppliers_and_retailers"><img alt="Research paper thumbnail of Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers" class="work-thumbnail" src="https://attachments.academia-assets.com/90641448/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86114976/Managing_supply_disruption_in_a_three_tier_supply_chain_with_multiple_suppliers_and_retailers">Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers</a></div><div class="wp-workCard_item"><span>2014 IEEE International Conference on Industrial Engineering and Engineering Management</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1dcff268e92045969eaa71ae80bd2b76" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641448,"asset_id":86114976,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641448/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114976"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114976"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114976; 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At first, we formulated a mathematical model for ideal conditions and then reformulated it to revise the supply, production and delivery plan after the occurrence of a disruption, for a future period, to recover from the disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We have also proposed an efficient heuristic to solve the model and the results have been compared, with another established solution approach, for a good number of randomly generated test problems. The comparison showed the consistent performance of our developed heuristic. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114975"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86114975/An_Inventory_Recovery_Model_for_an_Economic_Lot_Sizing_Problem_with_Disruption"><img alt="Research paper thumbnail of An Inventory Recovery Model for an Economic Lot Sizing Problem with Disruption" class="work-thumbnail" src="https://attachments.academia-assets.com/90641446/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86114975/An_Inventory_Recovery_Model_for_an_Economic_Lot_Sizing_Problem_with_Disruption">An Inventory Recovery Model for an Economic Lot Sizing Problem with Disruption</a></div><div class="wp-workCard_item"><span>Jurnal Teknologi</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Supply chains face risks from various unexpected events that make disruptions almost inevitable. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="98979ca67ee3561403b0d2e85a8de3b7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641446,"asset_id":86114975,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641446/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114975"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114975"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114975; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86114975]").text(description); $(".js-view-count[data-work-id=86114975]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86114975; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86114975']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86114975, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "98979ca67ee3561403b0d2e85a8de3b7" } } $('.js-work-strip[data-work-id=86114975]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86114975,"title":"An Inventory Recovery Model for an Economic Lot Sizing Problem with Disruption","translated_title":"","metadata":{"abstract":"Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.","publisher":"Penerbit UTM Press","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Jurnal Teknologi"},"translated_abstract":"Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.","internal_url":"https://www.academia.edu/86114975/An_Inventory_Recovery_Model_for_an_Economic_Lot_Sizing_Problem_with_Disruption","translated_internal_url":"","created_at":"2022-09-04T01:54:11.199-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641446,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641446/thumbnails/1.jpg","file_name":"f161.pdf","download_url":"https://www.academia.edu/attachments/90641446/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Inventory_Recovery_Model_for_an_Econo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641446/f161-libre.pdf?1662290990=\u0026response-content-disposition=attachment%3B+filename%3DAn_Inventory_Recovery_Model_for_an_Econo.pdf\u0026Expires=1732705643\u0026Signature=Isx-THnMOiog4O5mzHbwD~TQXh4AqOKMVMq3NcWi9n1-OzM6PZoO5FjYTYQZToGviBWgiJhNphwZ8VoW6Z7ADx1TZlXam82MSMhwupM6HFUfX9c9IL~EENg4FUAVDst2tYlncNNrV5t8ImRvOoq-uQlYaYktQIpCdfSGjTNXz22jPYzsyGtl~8BatjiqMOj2hTgw9hTj5SDydVODJuUtbWxgIFe204H8fUX7u7Oe094q5W~~OCg0HhsMe3X6VIqhR9x1AMk54As-Cpza8-bM2MUMliY0cSEGyYAUgpUmvskqf~7nQOjPFO1Ui9Y7EXxVNuySfqV7pkzqqYX~U7rdKA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Inventory_Recovery_Model_for_an_Economic_Lot_Sizing_Problem_with_Disruption","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641446,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641446/thumbnails/1.jpg","file_name":"f161.pdf","download_url":"https://www.academia.edu/attachments/90641446/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Inventory_Recovery_Model_for_an_Econo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641446/f161-libre.pdf?1662290990=\u0026response-content-disposition=attachment%3B+filename%3DAn_Inventory_Recovery_Model_for_an_Econo.pdf\u0026Expires=1732705643\u0026Signature=Isx-THnMOiog4O5mzHbwD~TQXh4AqOKMVMq3NcWi9n1-OzM6PZoO5FjYTYQZToGviBWgiJhNphwZ8VoW6Z7ADx1TZlXam82MSMhwupM6HFUfX9c9IL~EENg4FUAVDst2tYlncNNrV5t8ImRvOoq-uQlYaYktQIpCdfSGjTNXz22jPYzsyGtl~8BatjiqMOj2hTgw9hTj5SDydVODJuUtbWxgIFe204H8fUX7u7Oe094q5W~~OCg0HhsMe3X6VIqhR9x1AMk54As-Cpza8-bM2MUMliY0cSEGyYAUgpUmvskqf~7nQOjPFO1Ui9Y7EXxVNuySfqV7pkzqqYX~U7rdKA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics"},{"id":582376,"name":"Sizing","url":"https://www.academia.edu/Documents/in/Sizing"}],"urls":[{"id":23593445,"url":"https://jurnalteknologi.utm.my/index.php/jurnalteknologi/article/viewFile/9162/5459"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114974"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86114974/Managing_risk_and_disruption_in_production_inventory_and_supply_chain_systems_A_review"><img alt="Research paper thumbnail of Managing risk and disruption in production-inventory and supply chain systems: A review" class="work-thumbnail" src="https://attachments.academia-assets.com/90641453/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86114974/Managing_risk_and_disruption_in_production_inventory_and_supply_chain_systems_A_review">Managing risk and disruption in production-inventory and supply chain systems: A review</a></div><div class="wp-workCard_item"><span>Journal of Industrial and Management Optimization</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3039be8bf32c15e0b52c6d90d5a2e389" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641453,"asset_id":86114974,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641453/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114974"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114974"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114974; 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The review is conducted on the basis of comparing various works published in this research domain, specifically the papers, which considered real-life risk factors, such as imperfect production processes, risk and disruption in production, supply, demand, and transportation, while developing models for productioninventory and supply chain systems. Emphasis is given on the assumptions and the types of problems considered in the published research. We also focus on reviewing the mathematical models and the solution approaches used in solving the models using both hypothetical and real-world problem scenarios. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114973"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/86114973/Managing_risk_in_production_scheduling_under_uncertain_disruption"><img alt="Research paper thumbnail of Managing risk in production scheduling under uncertain disruption" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/86114973/Managing_risk_in_production_scheduling_under_uncertain_disruption">Managing risk in production scheduling under uncertain disruption</a></div><div class="wp-workCard_item"><span>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimizat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114973"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114973"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114973; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86114973]").text(description); $(".js-view-count[data-work-id=86114973]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86114973; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86114973']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86114973, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86114973]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86114973,"title":"Managing risk in production scheduling under uncertain disruption","translated_title":"","metadata":{"abstract":"The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. 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The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.","publisher":"Cambridge University Press (CUP)","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing"},"translated_abstract":"The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.","internal_url":"https://www.academia.edu/86114973/Managing_risk_in_production_scheduling_under_uncertain_disruption","translated_internal_url":"","created_at":"2022-09-04T01:54:10.844-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Managing_risk_in_production_scheduling_under_uncertain_disruption","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":96825,"name":"Manufacturing Engineering","url":"https://www.academia.edu/Documents/in/Manufacturing_Engineering"},{"id":554780,"name":"Interdisciplinary Engineering","url":"https://www.academia.edu/Documents/in/Interdisciplinary_Engineering"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114972"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/86114972/A_real_time_order_acceptance_and_scheduling_approach_for_permutation_flow_shop_problems"><img alt="Research paper thumbnail of A real-time order acceptance and scheduling approach for permutation flow shop problems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/86114972/A_real_time_order_acceptance_and_scheduling_approach_for_permutation_flow_shop_problems">A real-time order acceptance and scheduling approach for permutation flow shop problems</a></div><div class="wp-workCard_item"><span>European Journal of Operational Research</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimizat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimization problem. PFSP has been widely studied as a static problem using heuristics and metaheuristics. In reality, PFSPs are not usually static, but are rather dynamic, as customer orders are placed at random time intervals. In the dynamic problem, two tasks must be considered: (i) should a new order be accepted? and (ii) if accepted, how can this schedule be ordered, when some orders may be already under process and or be in the queue for processing? For the first task, we propose a simple heuristic based decision process, and for the second task, we developed a Genetic Algorithm (GA) based approach that is applied repeatedly for re-optimization as each new order arrives. The usefulness of the proposed approach has been demonstrated by solving a set of test problems. In addition the proposed approach, along with a simulation model, has been tested for maximizing the revenue of a flow shop production business under different order arrival scenarios. Finally, a case study is presented to show the applicability of the proposed approach in practice.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114972"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114972"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114972; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86114972]").text(description); $(".js-view-count[data-work-id=86114972]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86114972; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86114972']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86114972, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86114972]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86114972,"title":"A real-time order acceptance and scheduling approach for permutation flow shop problems","translated_title":"","metadata":{"abstract":"ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimization problem. PFSP has been widely studied as a static problem using heuristics and metaheuristics. In reality, PFSPs are not usually static, but are rather dynamic, as customer orders are placed at random time intervals. In the dynamic problem, two tasks must be considered: (i) should a new order be accepted? and (ii) if accepted, how can this schedule be ordered, when some orders may be already under process and or be in the queue for processing? For the first task, we propose a simple heuristic based decision process, and for the second task, we developed a Genetic Algorithm (GA) based approach that is applied repeatedly for re-optimization as each new order arrives. The usefulness of the proposed approach has been demonstrated by solving a set of test problems. In addition the proposed approach, along with a simulation model, has been tested for maximizing the revenue of a flow shop production business under different order arrival scenarios. Finally, a case study is presented to show the applicability of the proposed approach in practice.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"European Journal of Operational Research"},"translated_abstract":"ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimization problem. PFSP has been widely studied as a static problem using heuristics and metaheuristics. In reality, PFSPs are not usually static, but are rather dynamic, as customer orders are placed at random time intervals. In the dynamic problem, two tasks must be considered: (i) should a new order be accepted? and (ii) if accepted, how can this schedule be ordered, when some orders may be already under process and or be in the queue for processing? For the first task, we propose a simple heuristic based decision process, and for the second task, we developed a Genetic Algorithm (GA) based approach that is applied repeatedly for re-optimization as each new order arrives. The usefulness of the proposed approach has been demonstrated by solving a set of test problems. In addition the proposed approach, along with a simulation model, has been tested for maximizing the revenue of a flow shop production business under different order arrival scenarios. Finally, a case study is presented to show the applicability of the proposed approach in practice.","internal_url":"https://www.academia.edu/86114972/A_real_time_order_acceptance_and_scheduling_approach_for_permutation_flow_shop_problems","translated_internal_url":"","created_at":"2022-09-04T01:54:10.613-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_real_time_order_acceptance_and_scheduling_approach_for_permutation_flow_shop_problems","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":9049,"name":"Flow Shop Scheduling","url":"https://www.academia.edu/Documents/in/Flow_Shop_Scheduling"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":400356,"name":"Job shop scheduling","url":"https://www.academia.edu/Documents/in/Job_shop_scheduling"}],"urls":[{"id":23593443,"url":"https://api.elsevier.com/content/article/PII:S0377221715005329?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114958"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/86114958/Neurodynamic_differential_evolution_algorithm_and_solving_CEC2015_competition_problems"><img alt="Research paper thumbnail of Neurodynamic differential evolution algorithm and solving CEC2015 competition problems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/86114958/Neurodynamic_differential_evolution_algorithm_and_solving_CEC2015_competition_problems">Neurodynamic differential evolution algorithm and solving CEC2015 competition problems</a></div><div class="wp-workCard_item"><span>2015 IEEE Congress on Evolutionary Computation (CEC)</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Recently, the success history based parameter adaptation for differential evolution algo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is another recent approach that has shown remarkable convergence for certain problems, even for high dimensional cases. In this paper, we proposed a new algorithm by embedding the concept of neuro-dynamic into a modified success history based parameter adaptation for differential evolution with linear population size reduction. We have also proposed an adaptive mechanism for the appropriate use of the success history based parameter adaptation for differential evolution with linear population size reduction and neuro-dynamic during the search process. The new algorithm has been tested on the CEC’2015 single objective real-parameter competition problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from the success history based parameter adaptation for differential evolution with linear population size reduction and a few of the other state-of-the-art algorithms considered in this paper</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114958"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114958"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114958; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86114958]").text(description); $(".js-view-count[data-work-id=86114958]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86114958; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86114958']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86114958, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86114958]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86114958,"title":"Neurodynamic differential evolution algorithm and solving CEC2015 competition problems","translated_title":"","metadata":{"abstract":"ABSTRACT Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is another recent approach that has shown remarkable convergence for certain problems, even for high dimensional cases. In this paper, we proposed a new algorithm by embedding the concept of neuro-dynamic into a modified success history based parameter adaptation for differential evolution with linear population size reduction. We have also proposed an adaptive mechanism for the appropriate use of the success history based parameter adaptation for differential evolution with linear population size reduction and neuro-dynamic during the search process. The new algorithm has been tested on the CEC’2015 single objective real-parameter competition problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from the success history based parameter adaptation for differential evolution with linear population size reduction and a few of the other state-of-the-art algorithms considered in this paper","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"2015 IEEE Congress on Evolutionary Computation (CEC)"},"translated_abstract":"ABSTRACT Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is another recent approach that has shown remarkable convergence for certain problems, even for high dimensional cases. In this paper, we proposed a new algorithm by embedding the concept of neuro-dynamic into a modified success history based parameter adaptation for differential evolution with linear population size reduction. We have also proposed an adaptive mechanism for the appropriate use of the success history based parameter adaptation for differential evolution with linear population size reduction and neuro-dynamic during the search process. The new algorithm has been tested on the CEC’2015 single objective real-parameter competition problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from the success history based parameter adaptation for differential evolution with linear population size reduction and a few of the other state-of-the-art algorithms considered in this paper","internal_url":"https://www.academia.edu/86114958/Neurodynamic_differential_evolution_algorithm_and_solving_CEC2015_competition_problems","translated_internal_url":"","created_at":"2022-09-04T01:53:39.796-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Neurodynamic_differential_evolution_algorithm_and_solving_CEC2015_competition_problems","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation"},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution"}],"urls":[{"id":23593439,"url":"http://xplorestaging.ieee.org/ielx7/7229815/7256859/07257003.pdf?arnumber=7257003"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="56065404"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/56065404/Near_Optimal_Heuristics_for_Just_In_Time_Jobs_Maximization_in_Flow_Shop_Scheduling"><img alt="Research paper thumbnail of Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling" class="work-thumbnail" src="https://attachments.academia-assets.com/71634658/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/56065404/Near_Optimal_Heuristics_for_Just_In_Time_Jobs_Maximization_in_Flow_Shop_Scheduling">Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling</a></div><div class="wp-workCard_item"><span>Algorithms</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2fe8203c0c5a78379064aee27831ce43" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71634658,"asset_id":56065404,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71634658/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="56065404"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="56065404"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 56065404; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=56065404]").text(description); $(".js-view-count[data-work-id=56065404]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 56065404; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='56065404']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 56065404, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average) while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines), with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="2990832" id="papers"><div class="js-work-strip profile--work_container" data-work-id="86115013"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86115013/Evolutionary_Approaches_for_Project_Portfolio_Optimization_An_Overview"><img alt="Research paper thumbnail of Evolutionary Approaches for Project Portfolio Optimization: An Overview" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86115013/Evolutionary_Approaches_for_Project_Portfolio_Optimization_An_Overview">Evolutionary Approaches for Project Portfolio Optimization: An Overview</a></div><div class="wp-workCard_item"><span>Adaptation, Learning, and Optimization</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86115013"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86115013"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86115013; 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However, the strategies of balancing convergence and diversity and the effectiveness of handling problems with irregular Pareto fronts (PFs) are still far from perfect. To address these issues, this paper proposes an adaptive sorting-based evolutionary algorithm based on the idea of decomposition. First, we propose an adaptive sorting-based environmental selection strategy. Solutions in each subpopulation (partitioned by reference vectors) are sorted based on their convergence. Those with better convergence are further sorted based on their diversity, then being selected according to their sorting levels. Second, we provide an adaptive promising subpopulation sorting-based environmental selection strategy for problems which may have irregular PFs. This strategy provides additional sorting-based selection effort on promising subpopulations after the general environmental selection process. Third, we extend the algorithm to handle constraints. Finally, we conduct an extensive experimental study on the proposed algorithm by comparing with start-of-the-state algorithms. Results demonstrate the superiority of the proposed algorithm.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"IEEE Transactions on Evolutionary Computation","grobid_abstract_attachment_id":90641479},"translated_abstract":null,"internal_url":"https://www.academia.edu/86115011/Adaptive_Sorting_Based_Evolutionary_Algorithm_for_Many_Objective_Optimization","translated_internal_url":"","created_at":"2022-09-04T01:55:00.076-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641479,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641479/thumbnails/1.jpg","file_name":"f79fc996-6111-11e9-895b-00163e08bb86.pdf","download_url":"https://www.academia.edu/attachments/90641479/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Adaptive_Sorting_Based_Evolutionary_Algo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641479/f79fc996-6111-11e9-895b-00163e08bb86-libre.pdf?1662290994=\u0026response-content-disposition=attachment%3B+filename%3DAdaptive_Sorting_Based_Evolutionary_Algo.pdf\u0026Expires=1732705641\u0026Signature=K3XV36Usp4IXrc8QLkYrbzZouw-L4jKrtBy-0uhmzCtEQqRIKyHPlEnZH3Z6XfEgKQ1gV9nkknDVO2ZDlsdQYGvHbDbDkgKebNP2UY1ta4H2loOw8-yG0KsBtEMopEoc0nDjIrodDaR~V6fVcMqTfXlw3H2czOmaN6-O6zJCbVZw3ZrpHHlMwuyw553QtoSrU4J7xlu8KE1DbIfTEoRYHrKh3r3eBNlcHW2IYgIEawadCvcLbZGDHlUaw0keZdeDyh-HnlfO2eJQWP6o5ZgvLtS4vSvK7rbMsI134BMth2tGDhnzGHDNM~pWFh8LpkVYp2~CahZ~9KddXs0WWbtl2A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Adaptive_Sorting_Based_Evolutionary_Algorithm_for_Many_Objective_Optimization","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641479,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641479/thumbnails/1.jpg","file_name":"f79fc996-6111-11e9-895b-00163e08bb86.pdf","download_url":"https://www.academia.edu/attachments/90641479/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Adaptive_Sorting_Based_Evolutionary_Algo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641479/f79fc996-6111-11e9-895b-00163e08bb86-libre.pdf?1662290994=\u0026response-content-disposition=attachment%3B+filename%3DAdaptive_Sorting_Based_Evolutionary_Algo.pdf\u0026Expires=1732705642\u0026Signature=L7YcZDe6tV19LgfTuqepo9CaNVa66Rc9ZmirZAA3cs8g5icRJobPh84kPBk~G7kzNEldCxivmRR1FaiO3YWJFc9r6UNMswzcMA2NJFw7acdCDiLY2OE-KhoWyftpf6SkB64ipFDsOgh3xOl7v3tQKEuYBTSk7ZRPMWioMaPWk-Z5XIh0oc3IRaOpwpYq~OQrtPLNqJ1Lk4ExmCytF8QQL~ka~qkoTtxlGCMbyBWULJRlazaGhhb2sUlLfZ7SqE7VnKmOUF6h1I1XQqKCiF0aaLgoTbRLS22downaIaH64ZQ7X352EoRveUEGohSm~QXWKZdD5WXqUeEgskrhQjmDuQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization"},{"id":223634,"name":"Sorting","url":"https://www.academia.edu/Documents/in/Sorting"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"}],"urls":[{"id":23593465,"url":"http://xplorestaging.ieee.org/ielx7/4235/8676364/08387473.pdf?arnumber=8387473"}]}, dispatcherData: dispatcherData }); 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In this paper, it is formulated as a bi-level optimization problem in which, in the lower level, the community's social welfare is maximized by solving a power flow problem while, in the upper level, the profits of individual bidders are maximized. In this bidders' game, instead of using a set of discrete strategies as is usual, we consider continuous functions as strategies. To solve the upper-level problem, two co-evolutionary approaches are proposed and, for the lower level, an interior point algorithm is applied. Three IEEE benchmark problems in four different scenarios are solved and their results compared with those obtained from two conventional approaches and the literature which indicate that the proposed approaches have some merit regarding quality and efficiency.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Applied Soft Computing","grobid_abstract_attachment_id":90641476},"translated_abstract":null,"internal_url":"https://www.academia.edu/86115008/Co_evolutionary_approach_for_strategic_bidding_in_competitive_electricity_markets","translated_internal_url":"","created_at":"2022-09-04T01:54:58.247-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641476,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641476/thumbnails/1.jpg","file_name":"109681.pdf","download_url":"https://www.academia.edu/attachments/90641476/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Co_evolutionary_approach_for_strategic_b.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641476/109681-libre.pdf?1662290985=\u0026response-content-disposition=attachment%3B+filename%3DCo_evolutionary_approach_for_strategic_b.pdf\u0026Expires=1732705642\u0026Signature=T8tGC5Y66RDfPExELMnWwcWigqO0G9G8zBGXXzAXfeZQePqwUa8zARRK~ArschKWHndOcJHvbyaREjPrPN~IDwue3uZ3sAIHY7z4zdDMPLX-8AYdK752Xyg91TnOJ4BrzpCg0iBrXFnmLumjWEv5W48uTXky8SwmdvDhSJgvZaUC8ZpEXzJSaFnjjXWdK-1cKtpqaCGFEOHEYMyyIwu8794IlZPjN4tNVQFFRWZd8wVStr9SYyGFDNxj-iC~kRxSejD6WW45DF1TmJXG0SG5eWGIstiYrNjnTm3eZh5d0hqnKzaioTjmRWibYQOCo8fISUla2TFffVZNSmEHddHVfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Co_evolutionary_approach_for_strategic_bidding_in_competitive_electricity_markets","translated_slug":"","page_count":3,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641476,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641476/thumbnails/1.jpg","file_name":"109681.pdf","download_url":"https://www.academia.edu/attachments/90641476/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Co_evolutionary_approach_for_strategic_b.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641476/109681-libre.pdf?1662290985=\u0026response-content-disposition=attachment%3B+filename%3DCo_evolutionary_approach_for_strategic_b.pdf\u0026Expires=1732705642\u0026Signature=T8tGC5Y66RDfPExELMnWwcWigqO0G9G8zBGXXzAXfeZQePqwUa8zARRK~ArschKWHndOcJHvbyaREjPrPN~IDwue3uZ3sAIHY7z4zdDMPLX-8AYdK752Xyg91TnOJ4BrzpCg0iBrXFnmLumjWEv5W48uTXky8SwmdvDhSJgvZaUC8ZpEXzJSaFnjjXWdK-1cKtpqaCGFEOHEYMyyIwu8794IlZPjN4tNVQFFRWZd8wVStr9SYyGFDNxj-iC~kRxSejD6WW45DF1TmJXG0SG5eWGIstiYrNjnTm3eZh5d0hqnKzaioTjmRWibYQOCo8fISUla2TFffVZNSmEHddHVfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":38644,"name":"Bidding","url":"https://www.academia.edu/Documents/in/Bidding"}],"urls":[{"id":23593463,"url":"https://api.elsevier.com/content/article/PII:S1568494616306196?httpAccept=text/plain"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86115005"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86115005/Scenario_based_multi_period_program_optimization_for_capability_based_planning_using_evolutionary_algorithms"><img alt="Research paper thumbnail of Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/90641473/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86115005/Scenario_based_multi_period_program_optimization_for_capability_based_planning_using_evolutionary_algorithms">Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms</a></div><div class="wp-workCard_item"><span>Applied Soft Computing</span><span>, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3b8ce520d99ad54c42af109b1e035f99" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641473,"asset_id":86115005,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641473/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86115005"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86115005"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86115005; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "3b8ce520d99ad54c42af109b1e035f99" } } $('.js-work-strip[data-work-id=86115005]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86115005,"title":"Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"Capability-based planning (CBP) is a strategy focused planning framework that facilitates organizations to systematically develop capacity to achieve their business objectives in highly uncertain, dynamic and competitive environments. Capability programming is an integral part of CBP which requires selecting a portfolio of capability projects for execution, referred as a capability program, such that the overall strategic risk facing the planning organization across a number of projected future operating scenarios is minimized while maintaining the most economical choice. It is a challenging optimization problem that requires handling a number of dynamic constraints and objectives that vary throughout the entire planning horizon. An optimizing simulation approach is presented in this paper that combines an evolutionary multi-objective optimization algorithm with a reinforcement learning technique to generate capability programs which optimize strategic risks and program costs across multiple planning scenarios as well as over a rolling planning horizon. The role of the optimization algorithm in this approach is to search for the non-dominated capability programs at each decision point by minimizing the strategic risks associated with individual capability projects across a number of planning scenarios as well as the total cost of the program. The reinforcement learning algorithm, on the other hand, searches horizontally within the set of non-dominated programs to minimize capability risks and costs over the entire planning horizon. The methodology is evaluated on a test problem generated based on the data distributions in an Australian Defence Capability Plan and the performance is compared with two myopic heuristic methods.","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"Applied Soft Computing","grobid_abstract_attachment_id":90641473},"translated_abstract":null,"internal_url":"https://www.academia.edu/86115005/Scenario_based_multi_period_program_optimization_for_capability_based_planning_using_evolutionary_algorithms","translated_internal_url":"","created_at":"2022-09-04T01:54:56.774-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641473,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641473/thumbnails/1.jpg","file_name":"111183.pdf","download_url":"https://www.academia.edu/attachments/90641473/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Scenario_based_multi_period_program_opti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641473/111183-libre.pdf?1662290984=\u0026response-content-disposition=attachment%3B+filename%3DScenario_based_multi_period_program_opti.pdf\u0026Expires=1732705642\u0026Signature=BuEZZ2spyx0Ois9yKaVd9~bTtD1HTAiob6AiiWMYPuzEEFtk~m-xpZlunggIdKkT3Zmili1~Id7F86zJSNbMoeCWX-giofbkPE6HtjW76PUQIwZjhVZLZPMSDeui0Jd7cJqbNCYNhXC26tcrpR1Khn5aAKbr7ReXTUE2LuK7JpRgyUfAUXTDbn0R4A6TGAderOCzzGwkgJSZRRwiw1Jhz6q4n3HJ5GjjRoJou5JGYn46WmnYIsLpvBlmPYQrQmIOXM0RIQCNYAm5TF9Y5FeJamyiH8g9Q3K5oIDV4B4Wxri4cGwPuOLoyIhfeKpai6SigDXBZpDPwHpj0c~RcGh1Kg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Scenario_based_multi_period_program_optimization_for_capability_based_planning_using_evolutionary_algorithms","translated_slug":"","page_count":3,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641473,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641473/thumbnails/1.jpg","file_name":"111183.pdf","download_url":"https://www.academia.edu/attachments/90641473/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Scenario_based_multi_period_program_opti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641473/111183-libre.pdf?1662290984=\u0026response-content-disposition=attachment%3B+filename%3DScenario_based_multi_period_program_opti.pdf\u0026Expires=1732705642\u0026Signature=BuEZZ2spyx0Ois9yKaVd9~bTtD1HTAiob6AiiWMYPuzEEFtk~m-xpZlunggIdKkT3Zmili1~Id7F86zJSNbMoeCWX-giofbkPE6HtjW76PUQIwZjhVZLZPMSDeui0Jd7cJqbNCYNhXC26tcrpR1Khn5aAKbr7ReXTUE2LuK7JpRgyUfAUXTDbn0R4A6TGAderOCzzGwkgJSZRRwiw1Jhz6q4n3HJ5GjjRoJou5JGYn46WmnYIsLpvBlmPYQrQmIOXM0RIQCNYAm5TF9Y5FeJamyiH8g9Q3K5oIDV4B4Wxri4cGwPuOLoyIhfeKpai6SigDXBZpDPwHpj0c~RcGh1Kg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":23593461,"url":"https://api.elsevier.com/content/article/PII:S1568494616303349?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86115001"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/86115001/Investigating_Multi_Operator_Differential_Evolution_for_Feature_Selection"><img alt="Research paper thumbnail of Investigating Multi-Operator Differential Evolution for Feature Selection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/86115001/Investigating_Multi_Operator_Differential_Evolution_for_Feature_Selection">Investigating Multi-Operator Differential Evolution for Feature Selection</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Performance issues when dealing with a large number of features are well-known for classification...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. The algorithm partitions the initial population into a number of sub-populations evolving using a pool of distinct mutation strategies. Periodically, the sub-populations exchange information to enhance their diversity. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. Two classifiers, namely decision trees and k-nearest neighborhood, are used to evaluate the generated subsets of features. Experimental analysis has been conducted on several real data sets using a 10-fold cross validation. The analysis shows that the proposed algorithm successfully determines efficient feature subsets, which can improve the classification accuracy of the classifiers under consideration. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set, where the proposed method can effectively remove irrelevant features from the data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86115001"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86115001"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86115001; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86115001]").text(description); $(".js-view-count[data-work-id=86115001]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86115001; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86115001']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86115001, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86115001]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86115001,"title":"Investigating Multi-Operator Differential Evolution for Feature Selection","translated_title":"","metadata":{"abstract":"Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. The algorithm partitions the initial population into a number of sub-populations evolving using a pool of distinct mutation strategies. Periodically, the sub-populations exchange information to enhance their diversity. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. Two classifiers, namely decision trees and k-nearest neighborhood, are used to evaluate the generated subsets of features. Experimental analysis has been conducted on several real data sets using a 10-fold cross validation. The analysis shows that the proposed algorithm successfully determines efficient feature subsets, which can improve the classification accuracy of the classifiers under consideration. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set, where the proposed method can effectively remove irrelevant features from the data.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Lecture Notes in Computer Science"},"translated_abstract":"Performance issues when dealing with a large number of features are well-known for classification algorithms. Feature selection aims at mitigating these issues by reducing the number of features in the data. Hence, in this paper, a feature selection approach based on a multi-operator differential evolution algorithm is proposed. The algorithm partitions the initial population into a number of sub-populations evolving using a pool of distinct mutation strategies. Periodically, the sub-populations exchange information to enhance their diversity. This multi-operator approach reduces the sensitivity of the standard differential evolution to the selection of an appropriate mutation strategy. Two classifiers, namely decision trees and k-nearest neighborhood, are used to evaluate the generated subsets of features. Experimental analysis has been conducted on several real data sets using a 10-fold cross validation. The analysis shows that the proposed algorithm successfully determines efficient feature subsets, which can improve the classification accuracy of the classifiers under consideration. The usefulness of the proposed method on large scale data set has been demonstrated using the KDD Cup 1999 intrusion data set, where the proposed method can effectively remove irrelevant features from the data.","internal_url":"https://www.academia.edu/86115001/Investigating_Multi_Operator_Differential_Evolution_for_Feature_Selection","translated_internal_url":"","created_at":"2022-09-04T01:54:55.267-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Investigating_Multi_Operator_Differential_Evolution_for_Feature_Selection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1701,"name":"Evolutionary algorithms","url":"https://www.academia.edu/Documents/in/Evolutionary_algorithms"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution"},{"id":43619,"name":"Feature Selection","url":"https://www.academia.edu/Documents/in/Feature_Selection"},{"id":115676,"name":"Cyber Security","url":"https://www.academia.edu/Documents/in/Cyber_Security"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114998"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86114998/Solving_an_economic_and_environmental_dispatch_problem_using_evolutionary_algorithm"><img alt="Research paper thumbnail of Solving an economic and environmental dispatch problem using evolutionary algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/90641468/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86114998/Solving_an_economic_and_environmental_dispatch_problem_using_evolutionary_algorithm">Solving an economic and environmental dispatch problem using evolutionary algorithm</a></div><div class="wp-workCard_item"><span>2014 IEEE International Conference on Industrial Engineering and Engineering Management</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="744cff46512e09cef0969fd8df415d77" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641468,"asset_id":86114998,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641468/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114998"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114998"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114998; 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In this paper, we consider a power system with two types of generators, thermal and hydro. The characteristics of these generators vary with respect to the cost, emission to the environment, input source, capacity limit, and technological constraints. The mathematical model considering two objectives, such as minimization of the operating cost and minimization of total emissions, for a hydrothermal system is discussed. A solution approach has been proposed, based on evolutionary computation concept, for solving a benchmark problem for both single and bi-objective version of the problem. In the approach, an initial population of solutions is generated based on a heuristic and the population is then evolved using two well-known evolutionary search algorithms. The solutions of our approaches are compared with another approach from the literature. 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Since the 1960s many optimization algorithms have been proposed to solve this NP-hard problem, and their performances are evaluated in well-known test problems with different complexities. Although it is desirable to find an algorithm which can provide promising solutions with reasonable computational efforts for any problem under consideration, no single algorithm can meet that condition. To deal with this challenge, we present a genetic algorithm based memetic algorithm (MA) for solving RCPSP. The algorithm is initiated by a critical path-based heuristic and a variant of the Nawaz, Enscore, and Ham (NEH) heuristic. The algorithm involves a similar block order crossover and a variable insertion based local search. An automatic restart scheme is also presented which assists the algorithm to escape from local optima. In addition, a design-of-experiment (DOE) method is used to determine the set of suitable parameters for the proposed MA. Numerical results, statistical analysis and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed approach. 2 1 j (4) a {0, 1} jt (5) The objective function is the minimization of makespan, C max (Eq. (1)). Eq. (2) ensures that an activity can be executed only once. Eq. (3) ensures that each activity j cannot be started unless all its predecessors have been completed. Eq. (4) ensures that an activity can be started when its required renewable resources (such as workforce, machines, tools or equipment) are available. Over the years, exact techniques such as branch and bound [4-6], branch and cut [7], and the event based approach [8] have been proposed for the optimal solution of RCPSPs. 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In the first part of the paper, a mathematical model was developed for the optimization of replenishment and production decisions for each node after the occurrence of a transportation disruption. In addition, an experiment was conducted to study the effects of disruptions to the system using predefined scenarios, where the supplier’s prioritization of disruption mitigation strategies was explored. Various disruption scenarios were predefined by combining different disruption types and locations as well as different combinations of suppliers. It will be shown that the solution to the transportation disruption was more sensitive to the lot size when the lost sales cost was large. 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However, when the lost sales cost was low, the sensitivity to the lot size decreased, and the setup cost and inventory holding co...","publisher":"ICIS 2016","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"This paper presents the study of a real-time procurement and production mechanism for a three-stage supply chain system with multiple suppliers, subject to unexpected disruptions. In the first part of the paper, a mathematical model was developed for the optimization of replenishment and production decisions for each node after the occurrence of a transportation disruption. In addition, an experiment was conducted to study the effects of disruptions to the system using predefined scenarios, where the supplier’s prioritization of disruption mitigation strategies was explored. Various disruption scenarios were predefined by combining different disruption types and locations as well as different combinations of suppliers. It will be shown that the solution to the transportation disruption was more sensitive to the lot size when the lost sales cost was large. However, when the lost sales cost was low, the sensitivity to the lot size decreased, and the setup cost and inventory holding co...","internal_url":"https://www.academia.edu/86114978/Real_Time_disruption_management_in_a_coordinated_supply_chain_system_with_multiple_suppliers","translated_internal_url":"","created_at":"2022-09-04T01:54:11.944-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641450,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641450/thumbnails/1.jpg","file_name":"ILS2016_FB04_2.pdf","download_url":"https://www.academia.edu/attachments/90641450/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Real_Time_disruption_management_in_a_coo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641450/ILS2016_FB04_2-libre.pdf?1662290993=\u0026response-content-disposition=attachment%3B+filename%3DReal_Time_disruption_management_in_a_coo.pdf\u0026Expires=1732705643\u0026Signature=KWuDp7ZHXvxzSZ1kY2UkosyFQBbwr938gleaqS73Z9gZSIQS8Zbz30Re6qRqLnvkyO4mdT6I6sZttCHLSbNuBYRIhQt3Liotd3-xZaHgVqN~6uz7N6DRT22zT6o0M-NP5rJzMchynWQV3sQP6tQ-Pw3MC5Mes8zwjhi4xmj88PLIngv2D~xbcEFqArJ-6987HWcR3KH4foydcyJleyhZQOSjs1-jnQfGLf2c2ya~vr7TDvFJ5KekHdnJlCuUVEHFzamiZ6MUTl2wX0YeKNzFcLn~MvD9eu9Ob6YBUApSfNPeU819YERgMD2AbDHs~VFHmlW9tZIVfZF268kpCvyeHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Real_Time_disruption_management_in_a_coordinated_supply_chain_system_with_multiple_suppliers","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641450,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641450/thumbnails/1.jpg","file_name":"ILS2016_FB04_2.pdf","download_url":"https://www.academia.edu/attachments/90641450/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Real_Time_disruption_management_in_a_coo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641450/ILS2016_FB04_2-libre.pdf?1662290993=\u0026response-content-disposition=attachment%3B+filename%3DReal_Time_disruption_management_in_a_coo.pdf\u0026Expires=1732705643\u0026Signature=KWuDp7ZHXvxzSZ1kY2UkosyFQBbwr938gleaqS73Z9gZSIQS8Zbz30Re6qRqLnvkyO4mdT6I6sZttCHLSbNuBYRIhQt3Liotd3-xZaHgVqN~6uz7N6DRT22zT6o0M-NP5rJzMchynWQV3sQP6tQ-Pw3MC5Mes8zwjhi4xmj88PLIngv2D~xbcEFqArJ-6987HWcR3KH4foydcyJleyhZQOSjs1-jnQfGLf2c2ya~vr7TDvFJ5KekHdnJlCuUVEHFzamiZ6MUTl2wX0YeKNzFcLn~MvD9eu9Ob6YBUApSfNPeU819YERgMD2AbDHs~VFHmlW9tZIVfZF268kpCvyeHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":26,"name":"Business","url":"https://www.academia.edu/Documents/in/Business"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1335,"name":"Supply Chain Management","url":"https://www.academia.edu/Documents/in/Supply_Chain_Management"},{"id":24699,"name":"Supply Chain","url":"https://www.academia.edu/Documents/in/Supply_Chain"}],"urls":[{"id":23593448,"url":"http://ils2016conference.com/wp-content/uploads/2015/03/ILS2016_FB04_2.pdf"}]}, dispatcherData: dispatcherData }); 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Manually designing a CNN is a time-consuming process in regards to the various layers that it can have, and the variety of parameters that must be set up. Increasing the complexity of the network structure by employing various types of connections makes designing a network even more challenging. Evolutionary computation as an optimisation technique can be applied to arrange the CNN layers and/or initiate its parameters automatically or semi-automatically. Dense network and Residual network are two popular network structures that were introduced to facilitate the training of deep networks. In this paper, leveraging the potentials of Dense and Residual blocks, and using the capability of evolutionary computation, we propose an automatic evolutionary model to detect an optimum and accurate network structure and its parameters for medical image segmentation. The proposed evolutionary DenseRes model is employed for segmentation of six publicly available MRI and CT medical datasets. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114976"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86114976/Managing_supply_disruption_in_a_three_tier_supply_chain_with_multiple_suppliers_and_retailers"><img alt="Research paper thumbnail of Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers" class="work-thumbnail" src="https://attachments.academia-assets.com/90641448/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86114976/Managing_supply_disruption_in_a_three_tier_supply_chain_with_multiple_suppliers_and_retailers">Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers</a></div><div class="wp-workCard_item"><span>2014 IEEE International Conference on Industrial Engineering and Engineering Management</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1dcff268e92045969eaa71ae80bd2b76" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641448,"asset_id":86114976,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641448/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114976"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114976"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114976; 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At first, we formulated a mathematical model for ideal conditions and then reformulated it to revise the supply, production and delivery plan after the occurrence of a disruption, for a future period, to recover from the disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We have also proposed an efficient heuristic to solve the model and the results have been compared, with another established solution approach, for a good number of randomly generated test problems. The comparison showed the consistent performance of our developed heuristic. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114975"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86114975/An_Inventory_Recovery_Model_for_an_Economic_Lot_Sizing_Problem_with_Disruption"><img alt="Research paper thumbnail of An Inventory Recovery Model for an Economic Lot Sizing Problem with Disruption" class="work-thumbnail" src="https://attachments.academia-assets.com/90641446/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86114975/An_Inventory_Recovery_Model_for_an_Economic_Lot_Sizing_Problem_with_Disruption">An Inventory Recovery Model for an Economic Lot Sizing Problem with Disruption</a></div><div class="wp-workCard_item"><span>Jurnal Teknologi</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Supply chains face risks from various unexpected events that make disruptions almost inevitable. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="98979ca67ee3561403b0d2e85a8de3b7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641446,"asset_id":86114975,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641446/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114975"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114975"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114975; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86114975]").text(description); $(".js-view-count[data-work-id=86114975]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86114975; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86114975']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86114975, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "98979ca67ee3561403b0d2e85a8de3b7" } } $('.js-work-strip[data-work-id=86114975]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86114975,"title":"An Inventory Recovery Model for an Economic Lot Sizing Problem with Disruption","translated_title":"","metadata":{"abstract":"Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.","publisher":"Penerbit UTM Press","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Jurnal Teknologi"},"translated_abstract":"Supply chains face risks from various unexpected events that make disruptions almost inevitable. This paper presents a disruption recovery model for a single stage production and inventory system, where finished product supply is randomly disrupted for periods of random duration. A production facility that manufactures a single product following the Economic Production Quantity policy is considered. The model is solved using a search algorithm combined with a penalty function method to find the best recovery plan. It is shown that the optimal recovery schedule is dependent on the extent of the disruption, as well as the back order cost and lost sales cost parameters. The proposed model is seen to be a very useful tool for manufacturers to make quick decisions on the optimal recovery plan after the occurrence of a disruption.","internal_url":"https://www.academia.edu/86114975/An_Inventory_Recovery_Model_for_an_Economic_Lot_Sizing_Problem_with_Disruption","translated_internal_url":"","created_at":"2022-09-04T01:54:11.199-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":90641446,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641446/thumbnails/1.jpg","file_name":"f161.pdf","download_url":"https://www.academia.edu/attachments/90641446/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Inventory_Recovery_Model_for_an_Econo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641446/f161-libre.pdf?1662290990=\u0026response-content-disposition=attachment%3B+filename%3DAn_Inventory_Recovery_Model_for_an_Econo.pdf\u0026Expires=1732705643\u0026Signature=Isx-THnMOiog4O5mzHbwD~TQXh4AqOKMVMq3NcWi9n1-OzM6PZoO5FjYTYQZToGviBWgiJhNphwZ8VoW6Z7ADx1TZlXam82MSMhwupM6HFUfX9c9IL~EENg4FUAVDst2tYlncNNrV5t8ImRvOoq-uQlYaYktQIpCdfSGjTNXz22jPYzsyGtl~8BatjiqMOj2hTgw9hTj5SDydVODJuUtbWxgIFe204H8fUX7u7Oe094q5W~~OCg0HhsMe3X6VIqhR9x1AMk54As-Cpza8-bM2MUMliY0cSEGyYAUgpUmvskqf~7nQOjPFO1Ui9Y7EXxVNuySfqV7pkzqqYX~U7rdKA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"An_Inventory_Recovery_Model_for_an_Economic_Lot_Sizing_Problem_with_Disruption","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[{"id":90641446,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/90641446/thumbnails/1.jpg","file_name":"f161.pdf","download_url":"https://www.academia.edu/attachments/90641446/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"An_Inventory_Recovery_Model_for_an_Econo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/90641446/f161-libre.pdf?1662290990=\u0026response-content-disposition=attachment%3B+filename%3DAn_Inventory_Recovery_Model_for_an_Econo.pdf\u0026Expires=1732705643\u0026Signature=Isx-THnMOiog4O5mzHbwD~TQXh4AqOKMVMq3NcWi9n1-OzM6PZoO5FjYTYQZToGviBWgiJhNphwZ8VoW6Z7ADx1TZlXam82MSMhwupM6HFUfX9c9IL~EENg4FUAVDst2tYlncNNrV5t8ImRvOoq-uQlYaYktQIpCdfSGjTNXz22jPYzsyGtl~8BatjiqMOj2hTgw9hTj5SDydVODJuUtbWxgIFe204H8fUX7u7Oe094q5W~~OCg0HhsMe3X6VIqhR9x1AMk54As-Cpza8-bM2MUMliY0cSEGyYAUgpUmvskqf~7nQOjPFO1Ui9Y7EXxVNuySfqV7pkzqqYX~U7rdKA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics"},{"id":582376,"name":"Sizing","url":"https://www.academia.edu/Documents/in/Sizing"}],"urls":[{"id":23593445,"url":"https://jurnalteknologi.utm.my/index.php/jurnalteknologi/article/viewFile/9162/5459"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114974"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/86114974/Managing_risk_and_disruption_in_production_inventory_and_supply_chain_systems_A_review"><img alt="Research paper thumbnail of Managing risk and disruption in production-inventory and supply chain systems: A review" class="work-thumbnail" src="https://attachments.academia-assets.com/90641453/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/86114974/Managing_risk_and_disruption_in_production_inventory_and_supply_chain_systems_A_review">Managing risk and disruption in production-inventory and supply chain systems: A review</a></div><div class="wp-workCard_item"><span>Journal of Industrial and Management Optimization</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3039be8bf32c15e0b52c6d90d5a2e389" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90641453,"asset_id":86114974,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90641453/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114974"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114974"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114974; 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The review is conducted on the basis of comparing various works published in this research domain, specifically the papers, which considered real-life risk factors, such as imperfect production processes, risk and disruption in production, supply, demand, and transportation, while developing models for productioninventory and supply chain systems. Emphasis is given on the assumptions and the types of problems considered in the published research. We also focus on reviewing the mathematical models and the solution approaches used in solving the models using both hypothetical and real-world problem scenarios. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114973"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/86114973/Managing_risk_in_production_scheduling_under_uncertain_disruption"><img alt="Research paper thumbnail of Managing risk in production scheduling under uncertain disruption" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/86114973/Managing_risk_in_production_scheduling_under_uncertain_disruption">Managing risk in production scheduling under uncertain disruption</a></div><div class="wp-workCard_item"><span>Artificial Intelligence for Engineering Design, Analysis and Manufacturing</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimizat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114973"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114973"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114973; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86114973]").text(description); $(".js-view-count[data-work-id=86114973]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86114973; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86114973']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86114973, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86114973]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86114973,"title":"Managing risk in production scheduling under uncertain disruption","translated_title":"","metadata":{"abstract":"The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. 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The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.","publisher":"Cambridge University Press (CUP)","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing"},"translated_abstract":"The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.","internal_url":"https://www.academia.edu/86114973/Managing_risk_in_production_scheduling_under_uncertain_disruption","translated_internal_url":"","created_at":"2022-09-04T01:54:10.844-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Managing_risk_in_production_scheduling_under_uncertain_disruption","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":96825,"name":"Manufacturing Engineering","url":"https://www.academia.edu/Documents/in/Manufacturing_Engineering"},{"id":554780,"name":"Interdisciplinary Engineering","url":"https://www.academia.edu/Documents/in/Interdisciplinary_Engineering"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114972"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/86114972/A_real_time_order_acceptance_and_scheduling_approach_for_permutation_flow_shop_problems"><img alt="Research paper thumbnail of A real-time order acceptance and scheduling approach for permutation flow shop problems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/86114972/A_real_time_order_acceptance_and_scheduling_approach_for_permutation_flow_shop_problems">A real-time order acceptance and scheduling approach for permutation flow shop problems</a></div><div class="wp-workCard_item"><span>European Journal of Operational Research</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimizat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimization problem. PFSP has been widely studied as a static problem using heuristics and metaheuristics. In reality, PFSPs are not usually static, but are rather dynamic, as customer orders are placed at random time intervals. In the dynamic problem, two tasks must be considered: (i) should a new order be accepted? and (ii) if accepted, how can this schedule be ordered, when some orders may be already under process and or be in the queue for processing? For the first task, we propose a simple heuristic based decision process, and for the second task, we developed a Genetic Algorithm (GA) based approach that is applied repeatedly for re-optimization as each new order arrives. The usefulness of the proposed approach has been demonstrated by solving a set of test problems. In addition the proposed approach, along with a simulation model, has been tested for maximizing the revenue of a flow shop production business under different order arrival scenarios. Finally, a case study is presented to show the applicability of the proposed approach in practice.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114972"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114972"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114972; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86114972]").text(description); $(".js-view-count[data-work-id=86114972]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86114972; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86114972']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86114972, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86114972]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86114972,"title":"A real-time order acceptance and scheduling approach for permutation flow shop problems","translated_title":"","metadata":{"abstract":"ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimization problem. PFSP has been widely studied as a static problem using heuristics and metaheuristics. In reality, PFSPs are not usually static, but are rather dynamic, as customer orders are placed at random time intervals. In the dynamic problem, two tasks must be considered: (i) should a new order be accepted? and (ii) if accepted, how can this schedule be ordered, when some orders may be already under process and or be in the queue for processing? For the first task, we propose a simple heuristic based decision process, and for the second task, we developed a Genetic Algorithm (GA) based approach that is applied repeatedly for re-optimization as each new order arrives. The usefulness of the proposed approach has been demonstrated by solving a set of test problems. In addition the proposed approach, along with a simulation model, has been tested for maximizing the revenue of a flow shop production business under different order arrival scenarios. Finally, a case study is presented to show the applicability of the proposed approach in practice.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"European Journal of Operational Research"},"translated_abstract":"ABSTRACT The Permutation Flow Shop Scheduling Problem (PFSP) is a complex combinatorial optimization problem. PFSP has been widely studied as a static problem using heuristics and metaheuristics. In reality, PFSPs are not usually static, but are rather dynamic, as customer orders are placed at random time intervals. In the dynamic problem, two tasks must be considered: (i) should a new order be accepted? and (ii) if accepted, how can this schedule be ordered, when some orders may be already under process and or be in the queue for processing? For the first task, we propose a simple heuristic based decision process, and for the second task, we developed a Genetic Algorithm (GA) based approach that is applied repeatedly for re-optimization as each new order arrives. The usefulness of the proposed approach has been demonstrated by solving a set of test problems. In addition the proposed approach, along with a simulation model, has been tested for maximizing the revenue of a flow shop production business under different order arrival scenarios. Finally, a case study is presented to show the applicability of the proposed approach in practice.","internal_url":"https://www.academia.edu/86114972/A_real_time_order_acceptance_and_scheduling_approach_for_permutation_flow_shop_problems","translated_internal_url":"","created_at":"2022-09-04T01:54:10.613-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_real_time_order_acceptance_and_scheduling_approach_for_permutation_flow_shop_problems","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":9049,"name":"Flow Shop Scheduling","url":"https://www.academia.edu/Documents/in/Flow_Shop_Scheduling"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":400356,"name":"Job shop scheduling","url":"https://www.academia.edu/Documents/in/Job_shop_scheduling"}],"urls":[{"id":23593443,"url":"https://api.elsevier.com/content/article/PII:S0377221715005329?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="86114958"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/86114958/Neurodynamic_differential_evolution_algorithm_and_solving_CEC2015_competition_problems"><img alt="Research paper thumbnail of Neurodynamic differential evolution algorithm and solving CEC2015 competition problems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/86114958/Neurodynamic_differential_evolution_algorithm_and_solving_CEC2015_competition_problems">Neurodynamic differential evolution algorithm and solving CEC2015 competition problems</a></div><div class="wp-workCard_item"><span>2015 IEEE Congress on Evolutionary Computation (CEC)</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Recently, the success history based parameter adaptation for differential evolution algo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is another recent approach that has shown remarkable convergence for certain problems, even for high dimensional cases. In this paper, we proposed a new algorithm by embedding the concept of neuro-dynamic into a modified success history based parameter adaptation for differential evolution with linear population size reduction. We have also proposed an adaptive mechanism for the appropriate use of the success history based parameter adaptation for differential evolution with linear population size reduction and neuro-dynamic during the search process. The new algorithm has been tested on the CEC’2015 single objective real-parameter competition problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from the success history based parameter adaptation for differential evolution with linear population size reduction and a few of the other state-of-the-art algorithms considered in this paper</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="86114958"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="86114958"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 86114958; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=86114958]").text(description); $(".js-view-count[data-work-id=86114958]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 86114958; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='86114958']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 86114958, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=86114958]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":86114958,"title":"Neurodynamic differential evolution algorithm and solving CEC2015 competition problems","translated_title":"","metadata":{"abstract":"ABSTRACT Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is another recent approach that has shown remarkable convergence for certain problems, even for high dimensional cases. In this paper, we proposed a new algorithm by embedding the concept of neuro-dynamic into a modified success history based parameter adaptation for differential evolution with linear population size reduction. We have also proposed an adaptive mechanism for the appropriate use of the success history based parameter adaptation for differential evolution with linear population size reduction and neuro-dynamic during the search process. The new algorithm has been tested on the CEC’2015 single objective real-parameter competition problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from the success history based parameter adaptation for differential evolution with linear population size reduction and a few of the other state-of-the-art algorithms considered in this paper","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"2015 IEEE Congress on Evolutionary Computation (CEC)"},"translated_abstract":"ABSTRACT Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is another recent approach that has shown remarkable convergence for certain problems, even for high dimensional cases. In this paper, we proposed a new algorithm by embedding the concept of neuro-dynamic into a modified success history based parameter adaptation for differential evolution with linear population size reduction. We have also proposed an adaptive mechanism for the appropriate use of the success history based parameter adaptation for differential evolution with linear population size reduction and neuro-dynamic during the search process. The new algorithm has been tested on the CEC’2015 single objective real-parameter competition problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from the success history based parameter adaptation for differential evolution with linear population size reduction and a few of the other state-of-the-art algorithms considered in this paper","internal_url":"https://www.academia.edu/86114958/Neurodynamic_differential_evolution_algorithm_and_solving_CEC2015_competition_problems","translated_internal_url":"","created_at":"2022-09-04T01:53:39.796-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31714480,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Neurodynamic_differential_evolution_algorithm_and_solving_CEC2015_competition_problems","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":31714480,"first_name":"Ruhul","middle_initials":null,"last_name":"Sarker","page_name":"RuhulSarker","domain_name":"unsw","created_at":"2015-05-31T02:01:09.855-07:00","display_name":"Ruhul Sarker","url":"https://unsw.academia.edu/RuhulSarker"},"attachments":[],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3523,"name":"Evolutionary Computation","url":"https://www.academia.edu/Documents/in/Evolutionary_Computation"},{"id":12346,"name":"Differential Evolution","url":"https://www.academia.edu/Documents/in/Differential_Evolution"}],"urls":[{"id":23593439,"url":"http://xplorestaging.ieee.org/ielx7/7229815/7256859/07257003.pdf?arnumber=7257003"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="56065404"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/56065404/Near_Optimal_Heuristics_for_Just_In_Time_Jobs_Maximization_in_Flow_Shop_Scheduling"><img alt="Research paper thumbnail of Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling" class="work-thumbnail" src="https://attachments.academia-assets.com/71634658/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/56065404/Near_Optimal_Heuristics_for_Just_In_Time_Jobs_Maximization_in_Flow_Shop_Scheduling">Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling</a></div><div class="wp-workCard_item"><span>Algorithms</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2fe8203c0c5a78379064aee27831ce43" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":71634658,"asset_id":56065404,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/71634658/download_file?st=MTczMjcxNTYyNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="56065404"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="56065404"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 56065404; 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A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average) while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines), with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. 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