To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem | Journal of Computational and Cognitive Engineering

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem | Journal of Computational and Cognitive Engineering </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/4/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Journal of Computational and Cognitive Engineering"/> <meta name="citation_journal_abbrev" content="JCCE"/> <meta name="citation_issn" content="2810-9503"/> <meta name="citation_author" content="E. M. U. S. B. Ekanayake"/> <meta name="citation_author_institution" content="Department of Physical Sciences, Rajarata University of Sri Lanka, Sri Lanka"/> <meta name="citation_title" content="To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2023/02/02"/> <meta name="citation_volume" content="2"/> <meta name="citation_issue" content="4"/> <meta name="citation_firstpage" content="294"/> <meta name="citation_lastpage" content="303"/> <meta name="citation_doi" content="10.47852/bonviewJCCE3202378"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/378"/> <meta name="citation_abstract" xml:lang="en" content="Transportation problem (TP) aims to reduce the entire transportation cost of moving resources from various supply hubs to various demand hubs. However, in real-life situations, all organizations want to achieve numerous objectives while making transportation of goods. The degree of deterioration may vary depending on the mode, route, and time of transport. In some cases, the multiobjective could be used to reduce the use of a scarce resource, such as energy. As a result, the proposed approach was discovered to be an algorithm that improves bi- and triobjective TP techniques. This is an innovative way for solving the new bi- and triobjective transportation algorithm using a modified ant colony optimization (ACO) algorithm. According to the literature, various strategies have been developed in the past to tackle the multiobjective transportation problem (MOTP). The MOTP is solved using goal programming, fuzzy programming, interactive solution algorithms, and other techniques. These strategies are occasionally good or bad in achieving better results in a reasonable amount of time. The heuristic technique used in this work is the improved ACO algorithm, which is based on the ant colony algorithm and has been found to provide solutions with a reasonable degree of satisfaction for two and three objective TPs. When the findings are compared, the solution achieved using the proposed method has delivered the best performance and provides a case study to show the new strategy. 聽 Received: 19 September 2022 | Revised: 28 December 2022 | Accepted: 19 January 2023 聽 Conflicts of Interest The author declares that he has no conflicts of interest to this work."/> <meta name="citation_keywords" xml:lang="en" content="multi-objective"/> <meta name="citation_keywords" xml:lang="en" content="transportation problem"/> <meta name="citation_keywords" xml:lang="en" content="optimization"/> <meta name="citation_keywords" xml:lang="en" content=" efficient solution"/> <meta name="citation_keywords" xml:lang="en" content="ant colony optimization algorithm"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/download/378/298"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="E. M. U. S. B. 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This is an innovative way for solving the new bi- and triobjective transportation algorithm using a modified ant colony optimization (ACO) algorithm. According to the literature, various strategies have been developed in the past to tackle the multiobjective transportation problem (MOTP). The MOTP is solved using goal programming, fuzzy programming, interactive solution algorithms, and other techniques. These strategies are occasionally good or bad in achieving better results in a reasonable amount of time. The heuristic technique used in this work is the improved ACO algorithm, which is based on the ant colony algorithm and has been found to provide solutions with a reasonable degree of satisfaction for two and three objective TPs. When the findings are compared, the solution achieved using the proposed method has delivered the best performance and provides a case study to show the new strategy. 聽 Received: 19 September 2022 | Revised: 28 December 2022 | Accepted: 19 January 2023 聽 Conflicts of Interest The author declares that he has no conflicts of interest to this work."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="378"/> <meta name="DC.Identifier.pageNumber" content="294-303"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewJCCE3202378"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/378"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 Author"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0/"/> <meta name="DC.Source" content="Journal of Computational and Cognitive Engineering"/> <meta 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class="page_title"> To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> E. M. U. S. B. Ekanayake </span> <span class="affiliation"> Department of Physical Sciences, Rajarata University of Sri Lanka, Sri Lanka </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewJCCE3202378"> https://doi.org/10.47852/bonviewJCCE3202378 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> multi-objective, transportation problem, optimization, efficient solution, ant colony optimization algorithm </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Transportation problem (TP) aims to reduce the entire transportation cost of moving resources from various supply hubs to various demand hubs. However, in real-life situations, all organizations want to achieve numerous objectives while making transportation of goods. The degree of deterioration may vary depending on the mode, route, and time of transport. In some cases, the multiobjective could be used to reduce the use of a scarce resource, such as energy. As a result, the proposed approach was discovered to be an algorithm that improves bi- and triobjective TP techniques. This is an innovative way for solving the new bi- and triobjective transportation algorithm using a modified ant colony optimization (ACO) algorithm. According to the literature, various strategies have been developed in the past to tackle the multiobjective transportation problem (MOTP). The MOTP is solved using goal programming, fuzzy programming, interactive solution algorithms, and other techniques. These strategies are occasionally good or bad in achieving better results in a reasonable amount of time. The heuristic technique used in this work is the improved ACO algorithm, which is based on the ant colony algorithm and has been found to provide solutions with a reasonable degree of satisfaction for two and three objective TPs. When the findings are compared, the solution achieved using the proposed method has delivered the best performance and provides a case study to show the new strategy.</p> <p>聽</p> <p><strong>Received</strong>: 19 September 2022 | <strong>Revised</strong>: 28 December 2022 | <strong>Accepted</strong>: 19 January 2023</p> <p>聽</p> <p><strong>Conflicts of Interest</strong></p> <p>The author declares that he has no conflicts of interest to this work.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89267", "element":"#trendmd-suggestions"}'></script><div class="item downloads_chart"> <h3 class="label"> Metrics </h3> <div id="paperbuzz"><div id="loading">Metrics Loading ...</div></div> <script type="text/javascript"> window.onload = function () { var options = { paperbuzzStatsJson: JSON.parse('{\"altmetrics_sources\":[{\"events\":null,\"events_count\":264,\"events_count_by_day\":[{\"count\":\"1\",\"date\":\"2023-02-02\"},{\"count\":\"4\",\"date\":\"2023-02-07\"},{\"count\":\"1\",\"date\":\"2023-02-08\"},{\"count\":\"1\",\"date\":\"2023-02-10\"},{\"count\":\"1\",\"date\":\"2023-02-11\"},{\"count\":\"2\",\"date\":\"2023-02-16\"},{\"count\":\"1\",\"date\":\"2023-02-17\"},{\"count\":\"1\",\"date\":\"2023-02-20\"},{\"count\":\"1\",\"date\":\"2023-02-23\"},{\"count\":\"1\",\"date\":\"2023-02-25\"},{\"count\":\"1\",\"date\":\"2023-03-01\"}],\"events_count_by_month\":[{\"count\":\"14\",\"date\":\"2023-02\"},{\"count\":\"2\",\"date\":\"2023-03\"},{\"count\":\"4\",\"date\":\"2023-04\"},{\"count\":\"8\",\"date\":\"2023-05\"},{\"count\":\"8\",\"date\":\"2023-06\"},{\"count\":\"9\",\"date\":\"2023-07\"},{\"count\":\"5\",\"date\":\"2023-08\"},{\"count\":\"10\",\"date\":\"2023-09\"},{\"count\":\"7\",\"date\":\"2023-10\"},{\"count\":\"20\",\"date\":\"2023-11\"},{\"count\":\"34\",\"date\":\"2023-12\"},{\"count\":\"18\",\"date\":\"2024-01\"},{\"count\":\"10\",\"date\":\"2024-02\"},{\"count\":\"8\",\"date\":\"2024-03\"},{\"count\":\"11\",\"date\":\"2024-04\"},{\"count\":\"15\",\"date\":\"2024-05\"},{\"count\":\"8\",\"date\":\"2024-06\"},{\"count\":\"8\",\"date\":\"2024-07\"},{\"count\":\"11\",\"date\":\"2024-08\"},{\"count\":\"18\",\"date\":\"2024-09\"},{\"count\":\"7\",\"date\":\"2024-10\"},{\"count\":\"7\",\"date\":\"2024-11\"},{\"count\":\"15\",\"date\":\"2024-12\"},{\"count\":\"6\",\"date\":\"2025-01\"},{\"count\":\"1\",\"date\":\"2025-02\"}],\"events_count_by_year\":[{\"count\":\"121\",\"date\":\"2023\"},{\"count\":\"136\",\"date\":\"2024\"},{\"count\":\"7\",\"date\":\"2025\"}],\"source\":{\"display_name\":\"File downloads\"},\"source_id\":\"fileDownloads\"}],\"crossref_event_data_url\":\"https:\\/\\/api.eventdata.crossref.org\\/v1\\/events?rows=1000&filter=from-collected-date:1990-01-01,until-collected-date:2099-01-01,obj-id:10.47852\\/bonviewjcce3202378\",\"doi\":\"10.47852\\/bonviewjcce3202378\",\"metadata\":{\"DOI\":\"10.47852\\/bonviewjcce3202378\",\"ISSN\":[\"2810-9503\",\"2810-9570\"],\"URL\":\"http:\\/\\/dx.doi.org\\/10.47852\\/bonviewjcce3202378\",\"abstract\":\"<jats:p>Transportation problem (TP) aims to reduce the entire transportation cost of moving resources from various supply hubs to various demand hubs. 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