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heuristic</title> <meta name="description" content="Search results for: heuristic"> <meta name="keywords" content="heuristic"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="heuristic"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 246</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: heuristic</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">246</span> Heuristic to Generate Random X-Monotone Polygons</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamaljit%20Pati">Kamaljit Pati</a>, <a href="https://publications.waset.org/abstracts/search?q=Manas%20Kumar%20Mohanty"> Manas Kumar Mohanty</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjib%20Sadhu"> Sanjib Sadhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A heuristic has been designed to generate a random simple monotone polygon from a given set of ‘n’ points lying on a 2-Dimensional plane. Our heuristic generates a random monotone polygon in O(n) time after O(nℓogn) preprocessing time which is improved over the previous work where a random monotone polygon is produced in the same O(n) time but the preprocessing time is O(k) for n < k < n2. However, our heuristic does not generate all possible random polygons with uniform probability. The space complexity of our proposed heuristic is O(n). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sorting" title="sorting">sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=monotone%20polygon" title=" monotone polygon"> monotone polygon</a>, <a href="https://publications.waset.org/abstracts/search?q=visibility" title=" visibility"> visibility</a>, <a href="https://publications.waset.org/abstracts/search?q=chain" title=" chain"> chain</a> </p> <a href="https://publications.waset.org/abstracts/19252/heuristic-to-generate-random-x-monotone-polygons" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19252.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">245</span> A Combined Meta-Heuristic with Hyper-Heuristic Approach to Single Machine Production Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20E.%20Nugraheni">C. E. Nugraheni</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Abednego"> L. Abednego</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is concerned with minimization of mean tardiness and flow time in a real single machine production scheduling problem. Two variants of genetic algorithm as meta-heuristic are combined with hyper-heuristic approach are proposed to solve this problem. These methods are used to solve instances generated with real world data from a company. Encouraging results are reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyper-heuristics" title="hyper-heuristics">hyper-heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20scheduling" title=" production scheduling"> production scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic" title=" meta-heuristic"> meta-heuristic</a> </p> <a href="https://publications.waset.org/abstracts/12732/a-combined-meta-heuristic-with-hyper-heuristic-approach-to-single-machine-production-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12732.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">381</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">244</span> Schedule a New Production Plan by Heuristic Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hanife%20Merve%20%C3%96zt%C3%BCrk">Hanife Merve Öztürk</a>, <a href="https://publications.waset.org/abstracts/search?q=S%C4%B1d%C4%B1ka%20Dalgan"> Sıdıka Dalgan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this project, a capacity analysis study is done at TAT A. Ş. Maret Plant. Production capacity of products which generate 80% of sales amount are determined. Obtained data entered the LEKIN Scheduling Program and we get production schedules by using heuristic methods. Besides heuristic methods, as mathematical model, disjunctive programming formulation is adapted to flexible job shop problems by adding a new constraint to find optimal schedule solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=scheduling" title="scheduling">scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20problem" title=" flexible job shop problem"> flexible job shop problem</a>, <a href="https://publications.waset.org/abstracts/search?q=shifting%20bottleneck%20heuristic" title=" shifting bottleneck heuristic"> shifting bottleneck heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20modelling" title=" mathematical modelling"> mathematical modelling</a> </p> <a href="https://publications.waset.org/abstracts/13135/schedule-a-new-production-plan-by-heuristic-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13135.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">401</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">243</span> Heuristic Evaluation of Children’s Authoring Tool for Game Making</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laili%20Farhana%20Md%20Ibharim">Laili Farhana Md Ibharim</a>, <a href="https://publications.waset.org/abstracts/search?q=Maizatul%20Hayati%20Mohamad%20Yatim"> Maizatul Hayati Mohamad Yatim </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this study is to evaluate the heuristic inspection of children’s authoring tools to develop games. The researcher has selected 15 authoring tools for making games specifically for educational purposes. Nine students from Diploma of Game Design and Development course and four lecturers from the computing department involved in this evaluation. A set of usability heuristic checklist used as a guideline for the students and lecturers to observe and test the authoring tools selected. The study found that there are just a few authoring tools that fulfill most of the heuristic requirement and suitable to apply to children. In this evaluation, only six out of fifteen authoring tools have passed above than five elements in the heuristic inspection checklist. The researcher identified that in order to develop a usable authoring tool developer has to emphasis children acceptance and interaction of the authoring tool. Furthermore, the authoring tool can be a tool to enhance their mental development especially in creativity and skill. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authoring%20tool" title="authoring tool">authoring tool</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20making" title=" game making"> game making</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a> </p> <a href="https://publications.waset.org/abstracts/1364/heuristic-evaluation-of-childrens-authoring-tool-for-game-making" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1364.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">242</span> Improving the Global Competitiveness of SMEs by Logistics Transportation Management: Case Study Chicken Meat Supply Chain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Vanichkobchinda">P. Vanichkobchinda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Logistics Transportation techniques, Open Vehicle Routing (OVR) is an approach toward transportation cost reduction, especially for long distance pickup and delivery nodes. The outstanding characteristic of OVR is that the route starting node and ending node are not necessary the same as in typical vehicle routing problems. This advantage enables the routing to flow continuously and the vehicle does not always return to its home base. This research aims to develop a heuristic for the open vehicle routing problem with pickup and delivery under time window and loading capacity constraints to minimize the total distance. The proposed heuristic is developed based on the Insertion method, which is a simple method and suitable for the rapid calculation that allows insertion of the new additional transportation requirements along the original paths. According to the heuristic analysis, cost comparisons between the proposed heuristic and companies are using method, nearest neighbor method show that the insertion heuristic. Moreover, the proposed heuristic gave superior solutions in all types of test problems. In conclusion, the proposed heuristic can effectively and efficiently solve the open vehicle routing. The research indicates that the improvement of new transport's calculation and the open vehicle routing with "Insertion Heuristic" represent a better outcome with 34.3 percent in average. in cost savings. Moreover, the proposed heuristic gave superior solutions in all types of test problems. In conclusion, the proposed heuristic can effectively and efficiently solve the open vehicle routing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20competitiveness" title="business competitiveness">business competitiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20reduction" title=" cost reduction"> cost reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=SMEs" title=" SMEs"> SMEs</a>, <a href="https://publications.waset.org/abstracts/search?q=logistics%20transportation" title=" logistics transportation"> logistics transportation</a>, <a href="https://publications.waset.org/abstracts/search?q=VRP" title=" VRP"> VRP</a> </p> <a href="https://publications.waset.org/abstracts/25842/improving-the-global-competitiveness-of-smes-by-logistics-transportation-management-case-study-chicken-meat-supply-chain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25842.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">685</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">241</span> Heuristic for Scheduling Correlated Parallel Machine to Minimize Maximum Lateness and Total Weighed Completion Time</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang-Kuei%20Lin">Yang-Kuei Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yun-Xi%20Zhang"> Yun-Xi Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research focuses on the bicriteria correlated parallel machine scheduling problem. The two objective functions considered in this problem are to minimize maximum lateness and total weighted completion time. We first present a mixed integer programming (MIP) model that can find the entire efficient frontier for the studied problem. Next, we have proposed a bicriteria heuristic that can find non-dominated solutions for the studied problem. The performance of the proposed bicriteria heuristic is compared with the efficient frontier generated by solving the MIP model. Computational results indicate that the proposed bicriteria heuristic can solve the problem efficiently and find a set of diverse solutions that are uniformly distributed along the efficient frontier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bicriteria" title="bicriteria">bicriteria</a>, <a href="https://publications.waset.org/abstracts/search?q=correlated%20parallel%20machines" title=" correlated parallel machines"> correlated parallel machines</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a> </p> <a href="https://publications.waset.org/abstracts/108546/heuristic-for-scheduling-correlated-parallel-machine-to-minimize-maximum-lateness-and-total-weighed-completion-time" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108546.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">240</span> A Heuristic Approach for the General Flowshop Scheduling Problem to Minimize the Makespan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Ziaee">Mohsen Ziaee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Almost all existing researches on the flowshop scheduling problems focus on the permutation schedules and there is insufficient study dedicated to the general flowshop scheduling problems in the literature, since the modeling and solving of the general flowshop scheduling problems are more difficult than the permutation ones, especially for the large-size problem instances. This paper considers the general flowshop scheduling problem with the objective function of the makespan (F//Cmax). We first find the optimal solution of the problem by solving a mixed integer linear programming model. An efficient heuristic method is then presented to solve the problem. An ant colony optimization algorithm is also proposed for the problem. In order to evaluate the performance of the methods, computational experiments are designed and performed. Numerical results show that the heuristic algorithm can result in reasonable solutions with low computational effort and even achieve optimal solutions in some cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=scheduling" title="scheduling">scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20flow%20shop%20scheduling%20problem" title=" general flow shop scheduling problem"> general flow shop scheduling problem</a>, <a href="https://publications.waset.org/abstracts/search?q=makespan" title=" makespan"> makespan</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a> </p> <a href="https://publications.waset.org/abstracts/92277/a-heuristic-approach-for-the-general-flowshop-scheduling-problem-to-minimize-the-makespan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92277.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">207</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">239</span> A Heuristic for the Integrated Production and Distribution Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christian%20Meinecke">Christian Meinecke</a>, <a href="https://publications.waset.org/abstracts/search?q=Bernd%20Scholz-Reiter"> Bernd Scholz-Reiter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The integrated problem of production and distribution scheduling is relevant in many industrial applications. Thus, many heuristics to solve this integrated problem have been developed in the last decade. Most of these heuristics use a sequential working principal or a single decomposition and integration approach to separate and solve sub-problems. A heuristic using a multi-step decomposition and integration approach is presented in this paper and evaluated in a case study. The result show significant improved results compared with sequential scheduling heuristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=production%20and%20outbound%20distribution" title="production and outbound distribution">production and outbound distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20planning" title=" integrated planning"> integrated planning</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=decomposition" title=" decomposition"> decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=integration" title=" integration"> integration</a> </p> <a href="https://publications.waset.org/abstracts/4364/a-heuristic-for-the-integrated-production-and-distribution-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4364.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">429</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">238</span> An Approach to Improve Pre University Students' Responsible Environmental Behaviour through Science Writing Heuristic in Malaysia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheila%20Shamuganathan">Sheila Shamuganathan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mageswary%20Karpudewan"> Mageswary Karpudewan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigated the effectiveness of green chemistry integrated with Science Writing Heuristic (SWH) in enhancing matriculation students’ responsible environmental behaviour. For this purpose 207 matriculation students were randomly assigned into experimental (N=118) and control (N=89) group. For the experimental group the chemistry concepts were taught using the instructional approach of green chemistry integrated with Science Writing Heuristic (SWH) while for the control group the same content was taught using green chemistry. The data was analysed using ANCOVA and findings obtained from the quantitative analysis reveals that there is significant changes in responsible environmental behaviour (F 1,204) = 32.13 (ηp² = 0.14) which favours the experimental group. The responses of the qualitative data obtained from an interview with the experimental group also further strengthen and indicated a significant improvement in responsible environmental behaviour. The outcome of the study suggests that using green chemistry integrated with Science Writing Heuristic (SWH) could be an alternative approach to improve students’ responsible environmental behaviour towards the environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=science%20writing%20heuristic" title="science writing heuristic">science writing heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20chemistry" title=" green chemistry"> green chemistry</a>, <a href="https://publications.waset.org/abstracts/search?q=pro%20environmental%20behaviour" title=" pro environmental behaviour"> pro environmental behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=laboratory" title=" laboratory"> laboratory</a> </p> <a href="https://publications.waset.org/abstracts/58798/an-approach-to-improve-pre-university-students-responsible-environmental-behaviour-through-science-writing-heuristic-in-malaysia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58798.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">237</span> Extension of a Competitive Location Model Considering a Given Number of Servers and Proposing a Heuristic for Solving</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Seifbarghy">Mehdi Seifbarghy</a>, <a href="https://publications.waset.org/abstracts/search?q=Zahra%20Nasiri"> Zahra Nasiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Competitive location problem deals with locating new facilities to provide a service (or goods) to the customers of a given geographical area where other facilities (competitors) offering the same service are already present. The new facilities will have to compete with the existing facilities for capturing the market share. This paper proposes a new model to maximize the market share in which customers choose the facilities based on traveling time, waiting time and attractiveness. The attractiveness of a facility is considered as a parameter in the model. A heuristic is proposed to solve the problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=competitive%20location" title="competitive location">competitive location</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20share" title=" market share"> market share</a>, <a href="https://publications.waset.org/abstracts/search?q=facility%20attractiveness" title=" facility attractiveness"> facility attractiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a> </p> <a href="https://publications.waset.org/abstracts/31377/extension-of-a-competitive-location-model-considering-a-given-number-of-servers-and-proposing-a-heuristic-for-solving" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31377.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">523</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">236</span> Curve Fitting by Cubic Bezier Curves Using Migrating Birds Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Bezier%20curves" title=" Bezier curves"> Bezier curves</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20optimization" title=" heuristic optimization"> heuristic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migrating%20birds%20optimization" title=" migrating birds optimization"> migrating birds optimization</a> </p> <a href="https://publications.waset.org/abstracts/78026/curve-fitting-by-cubic-bezier-curves-using-migrating-birds-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78026.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">337</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">235</span> Two Efficient Heuristic Algorithms for the Integrated Production Planning and Warehouse Layout Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Pourmohammadi%20Fallah">Mohammad Pourmohammadi Fallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Maziar%20Salahi"> Maziar Salahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the literature, a mixed-integer linear programming model for the integrated production planning and warehouse layout problem is proposed. To solve the model, the authors proposed a Lagrangian relax-and-fix heuristic that takes a significant amount of time to stop with gaps above 5$\%$ for large-scale instances. Here, we present two heuristic algorithms to solve the problem. In the first one, we use a greedy approach by allocating warehouse locations with less reservation costs and also less transportation costs from the production area to locations and from locations to the output point to items with higher demands. Then a smaller model is solved. In the second heuristic, first, we sort items in descending order according to the fraction of the sum of the demands for that item in the time horizon plus the maximum demand for that item in the time horizon and the sum of all its demands in the time horizon. Then we categorize the sorted items into groups of 3, 4, or 5 and solve a small-scale optimization problem for each group, hoping to improve the solution of the first heuristic. Our preliminary numerical results show the effectiveness of the proposed heuristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=capacitated%20lot-sizing" title="capacitated lot-sizing">capacitated lot-sizing</a>, <a href="https://publications.waset.org/abstracts/search?q=warehouse%20layout" title=" warehouse layout"> warehouse layout</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-integer%20linear%20programming" title=" mixed-integer linear programming"> mixed-integer linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics%20algorithm" title=" heuristics algorithm"> heuristics algorithm</a> </p> <a href="https://publications.waset.org/abstracts/154415/two-efficient-heuristic-algorithms-for-the-integrated-production-planning-and-warehouse-layout-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154415.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">234</span> Order Picking Problem: An Exact and Heuristic Algorithms for the Generalized Travelling Salesman Problem With Geographical Overlap Between Clusters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farzaneh%20Rajabighamchi">Farzaneh Rajabighamchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Stan%20van%20Hoesel"> Stan van Hoesel</a>, <a href="https://publications.waset.org/abstracts/search?q=Christof%20Defryn"> Christof Defryn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The generalized traveling salesman problem (GTSP) is an extension of the traveling salesman problem (TSP) where the set of nodes is partitioned into clusters, and the salesman must visit exactly one node per cluster. In this research, we apply the definition of the GTSP to an order picker routing problem with multiple locations per product. As such, each product represents a cluster and its corresponding nodes are the locations at which the product can be retrieved. To pick a certain product item from the warehouse, the picker needs to visit one of these locations during its pick tour. As all products are scattered throughout the warehouse, the product clusters not separated geographically. We propose an exact LP model as well as heuristic and meta-heuristic solution algorithms for the order picking problem with multiple product locations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=warehouse%20optimization" title="warehouse optimization">warehouse optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=order%20picking%20problem" title=" order picking problem"> order picking problem</a>, <a href="https://publications.waset.org/abstracts/search?q=generalised%20travelling%20salesman%20problem" title=" generalised travelling salesman problem"> generalised travelling salesman problem</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithm" title=" heuristic algorithm"> heuristic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/151459/order-picking-problem-an-exact-and-heuristic-algorithms-for-the-generalized-travelling-salesman-problem-with-geographical-overlap-between-clusters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">112</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">233</span> Comparison of Heuristic Methods for Solving Traveling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Regita%20P.%20Permata">Regita P. Permata</a>, <a href="https://publications.waset.org/abstracts/search?q=Ulfa%20S.%20Nuraini"> Ulfa S. Nuraini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traveling Salesman Problem (TSP) is the most studied problem in combinatorial optimization. In simple language, TSP can be described as a problem of finding a minimum distance tour to a city, starting and ending in the same city, and exactly visiting another city. In product distribution, companies often get problems in determining the minimum distance that affects the time allocation. In this research, we aim to apply TSP heuristic methods to simulate nodes as city coordinates in product distribution. The heuristics used are sub tour reversal, nearest neighbor, farthest insertion, cheapest insertion, nearest insertion, and arbitrary insertion. We have done simulation nodes using Euclidean distances to compare the number of cities and processing time, thus we get optimum heuristic method. The results show that the optimum heuristic methods are farthest insertion and nearest insertion. These two methods can be recommended to solve product distribution problems in certain companies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Euclidean" title="Euclidean">Euclidean</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=TSP" title=" TSP"> TSP</a> </p> <a href="https://publications.waset.org/abstracts/129149/comparison-of-heuristic-methods-for-solving-traveling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129149.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">232</span> Optimal Planning of Dispatchable Distributed Generators for Power Loss Reduction in Unbalanced Distribution Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20M.%20Othman">Mahmoud M. Othman</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20G.%20Hegazy"> Y. G. Hegazy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Y.%20Abdelaziz"> A. Y. Abdelaziz </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation" title="distributed generation">distributed generation</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20approach" title=" heuristic approach"> heuristic approach</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=planning" title=" planning"> planning</a> </p> <a href="https://publications.waset.org/abstracts/31208/optimal-planning-of-dispatchable-distributed-generators-for-power-loss-reduction-in-unbalanced-distribution-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31208.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">525</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">231</span> Application of Heuristic Integration Ant Colony Optimization in Path Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zeyu%20Zhang">Zeyu Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Guisheng%20Yin"> Guisheng Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziying%20Zhang"> Ziying Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Liguo%20Zhang"> Liguo Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper mainly studies the path planning method based on ant colony optimization (ACO), and proposes heuristic integration ant colony optimization (HIACO). This paper not only analyzes and optimizes the principle, but also simulates and analyzes the parameters related to the application of HIACO in path planning. Compared with the original algorithm, the improved algorithm optimizes probability formula, tabu table mechanism and updating mechanism, and introduces more reasonable heuristic factors. The optimized HIACO not only draws on the excellent ideas of the original algorithm, but also solves the problems of premature convergence, convergence to the sub optimal solution and improper exploration to some extent. HIACO can be used to achieve better simulation results and achieve the desired optimization. Combined with the probability formula and update formula, several parameters of HIACO are tested. This paper proves the principle of the HIACO and gives the best parameter range in the research of path planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization" title="ant colony optimization">ant colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20integration" title=" heuristic integration"> heuristic integration</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20formula" title=" probability formula"> probability formula</a> </p> <a href="https://publications.waset.org/abstracts/115269/application-of-heuristic-integration-ant-colony-optimization-in-path-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115269.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">251</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">230</span> A Hybrid Heuristic for the Team Orienteering Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Bouchakhchoukha">Adel Bouchakhchoukha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hakim%20Akeb"> Hakim Akeb </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we propose a hybrid heuristic in order to solve the Team Orienteering Problem (TOP). Given a set of points (or customers), each with associated score (profit or benefit), and a team that has a fixed number of members, the problem to solve is to visit a subset of points in order to maximize the total collected score. Each member performs a tour starting at the start point, visiting distinct customers and the tour terminates at the arrival point. In addition, each point is visited at most once, and the total time in each tour cannot be greater than a given value. The proposed heuristic combines beam search and a local optimization strategy. The algorithm was tested on several sets of instances and encouraging results were obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=team%20orienteering%20problem" title="team orienteering problem">team orienteering problem</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20routing" title=" vehicle routing"> vehicle routing</a>, <a href="https://publications.waset.org/abstracts/search?q=beam%20search" title=" beam search"> beam search</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a> </p> <a href="https://publications.waset.org/abstracts/12948/a-hybrid-heuristic-for-the-team-orienteering-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12948.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">229</span> Design and Optimization of Open Loop Supply Chain Distribution Network Using Hybrid K-Means Cluster Based Heuristic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Suresh">P. Suresh</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Gunasekaran"> K. Gunasekaran</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Thanigaivelan"> R. Thanigaivelan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RFID" title="RFID">RFID</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20distribution%20network" title=" supply chain distribution network"> supply chain distribution network</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20loop%20supply%20chain" title=" open loop supply chain"> open loop supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a> </p> <a href="https://publications.waset.org/abstracts/110012/design-and-optimization-of-open-loop-supply-chain-distribution-network-using-hybrid-k-means-cluster-based-heuristic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110012.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">165</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">228</span> Personas Help Understand Users’ Needs, Goals and Desires in an Online Institutional Repository</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maha%20ALjohani">Maha ALjohani</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Blustein"> James Blustein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Communicating users' needs, goals and problems help designers and developers overcome challenges faced by end users. Personas are used to represent end users’ needs. In our research, creating personas allowed the following questions to be answered: Who are the potential user groups? What do they want to achieve by using the service? What are the problems that users face? What should the service provide to them? To develop realistic personas, we conducted a focus group discussion with undergraduate and graduate students and also interviewed a university librarian. The personas were created to help evaluating the Institutional Repository that is based on the DSpace system. The profiles helped to communicate users' needs, abilities, tasks, and problems, and the task scenarios used in the heuristic evaluation were based on these personas. Four personas resulted of a focus group discussion with undergraduate and graduate students and from interviewing a university librarian. We then used these personas to create focused task-scenarios for a heuristic evaluation on the system interface to ensure that it met users' needs, goals, problems and desires. In this paper, we present the process that we used to create the personas that led to devise the task scenarios used in the heuristic evaluation as a follow up study of the DSpace university repository. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heuristic%20evaluation" title="heuristic evaluation">heuristic evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=institutional%20repositories" title=" institutional repositories"> institutional repositories</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20experience" title=" user experience"> user experience</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20computer%20interaction" title=" human computer interaction"> human computer interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20profiles" title=" user profiles"> user profiles</a>, <a href="https://publications.waset.org/abstracts/search?q=personas" title=" personas"> personas</a>, <a href="https://publications.waset.org/abstracts/search?q=task%20scenarios" title=" task scenarios"> task scenarios</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a> </p> <a href="https://publications.waset.org/abstracts/13758/personas-help-understand-users-needs-goals-and-desires-in-an-online-institutional-repository" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13758.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">499</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">227</span> A Heuristic Based Decomposition Approach for a Hierarchical Production Planning Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nusrat%20T.%20Chowdhury">Nusrat T. Chowdhury</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20F.%20Baki"> M. F. Baki</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Azab"> A. Azab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The production planning problem is concerned with specifying the optimal quantities to produce in order to meet the demand for a prespecified planning horizon with the least possible expenditure. Making the right decisions in production planning will affect directly the performance and productivity of a manufacturing firm, which is important for its ability to compete in the market. Therefore, developing and improving solution procedures for production planning problems is very significant. In this paper, we develop a Dantzig-Wolfe decomposition of a multi-item hierarchical production planning problem with capacity constraint and present a column generation approach to solve the problem. The original Mixed Integer Linear Programming model of the problem is decomposed item by item into a master problem and a number of subproblems. The capacity constraint is considered as the linking constraint between the master problem and the subproblems. The subproblems are solved using the dynamic programming approach. We also propose a multi-step iterative capacity allocation heuristic procedure to handle any kind of infeasibility that arises while solving the problem. We compare the computational performance of the developed solution approach against the state-of-the-art heuristic procedure available in the literature. The results show that the proposed heuristic-based decomposition approach improves the solution quality by 20% as compared to the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inventory" title="inventory">inventory</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-level%20capacitated%20lot-sizing" title=" multi-level capacitated lot-sizing"> multi-level capacitated lot-sizing</a>, <a href="https://publications.waset.org/abstracts/search?q=emission%20control" title=" emission control"> emission control</a>, <a href="https://publications.waset.org/abstracts/search?q=setup%20carryover" title=" setup carryover"> setup carryover</a> </p> <a href="https://publications.waset.org/abstracts/112830/a-heuristic-based-decomposition-approach-for-a-hierarchical-production-planning-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112830.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">226</span> An Algorithm for the Map Labeling Problem with Two Kinds of Priorities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noboru%20Abe">Noboru Abe</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshinori%20Amai"> Yoshinori Amai</a>, <a href="https://publications.waset.org/abstracts/search?q=Toshinori%20Nakatake"> Toshinori Nakatake</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumio%20Masuda"> Sumio Masuda</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazuaki%20Yamaguchi"> Kazuaki Yamaguchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We consider the problem of placing labels of the points on a plane. For each point, its position, the size of its label and a priority are given. Moreover, several candidates of its label positions are prespecified, and each of such label positions is assigned a priority. The objective of our problem is to maximize the total sum of priorities of placed labels and their points. By refining a labeling algorithm that can use these priorities, we propose a new heuristic algorithm which is more suitable for treating the assigned priorities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=map%20labeling" title="map labeling">map labeling</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20algorithm" title=" greedy algorithm"> greedy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithm" title=" heuristic algorithm"> heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=priority" title=" priority"> priority</a> </p> <a href="https://publications.waset.org/abstracts/5013/an-algorithm-for-the-map-labeling-problem-with-two-kinds-of-priorities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5013.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">225</span> Bee Colony Optimization Applied to the Bin Packing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kenza%20Aida%20Amara">Kenza Aida Amara</a>, <a href="https://publications.waset.org/abstracts/search?q=Bachir%20Djebbar"> Bachir Djebbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We treat the two-dimensional bin packing problem which involves packing a given set of rectangles into a minimum number of larger identical rectangles called bins. This combinatorial problem is NP-hard. We propose a pretreatment for the oriented version of the problem that allows the valorization of the lost areas in the bins and the reduction of the size problem. A heuristic method based on the strategy first-fit adapted to this problem is presented. We present an approach of resolution by bee colony optimization. Computational results express a comparison of the number of bins used with and without pretreatment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bee%20colony%20optimization" title="bee colony optimization">bee colony optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=bin%20packing" title=" bin packing"> bin packing</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithm" title=" heuristic algorithm"> heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=pretreatment" title=" pretreatment"> pretreatment</a> </p> <a href="https://publications.waset.org/abstracts/65005/bee-colony-optimization-applied-to-the-bin-packing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">634</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">224</span> Survey Paper on Graph Coloring Problem and Its Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prateek%20Chharia">Prateek Chharia</a>, <a href="https://publications.waset.org/abstracts/search?q=Biswa%20Bhusan%20Ghosh"> Biswa Bhusan Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graph coloring is one of the prominent concepts in graph coloring. It can be defined as a coloring of the various regions of the graph such that all the constraints are fulfilled. In this paper various graphs coloring approaches like greedy coloring, Heuristic search for maximum independent set and graph coloring using edge table is described. Graph coloring can be used in various real time applications like student time tabling generation, Sudoku as a graph coloring problem, GSM phone network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20coloring" title="graph coloring">graph coloring</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20coloring" title=" greedy coloring"> greedy coloring</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20search" title=" heuristic search"> heuristic search</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20table" title=" edge table"> edge table</a>, <a href="https://publications.waset.org/abstracts/search?q=sudoku%20as%20a%20graph%20coloring%20problem" title=" sudoku as a graph coloring problem"> sudoku as a graph coloring problem</a> </p> <a href="https://publications.waset.org/abstracts/19691/survey-paper-on-graph-coloring-problem-and-its-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19691.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">539</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">223</span> A Coordinate-Based Heuristic Route Search Algorithm for Delivery Truck Routing Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Tarek">Ahmed Tarek</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alveed"> Ahmed Alveed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vehicle routing problem is a well-known re-search avenue in computing. Modern vehicle routing is more focused with the GPS-based coordinate system, as the state-of-the-art vehicle, and trucking systems are equipped with digital navigation. In this paper, a new two dimensional coordinate-based algorithm for addressing the vehicle routing problem for a supply chain network is proposed and explored, and the algorithm is compared with other available, and recently devised heuristics. For the algorithms discussed, which includes the pro-posed coordinate-based search heuristic as well, the advantages and the disadvantages associated with the heuristics are explored. The proposed algorithm is studied from the stand point of a small supermarket chain delivery network that supplies to its stores in four different states around the East Coast area, and is trying to optimize its trucking delivery cost. Minimizing the delivery cost for the supply network of a supermarket chain is important to ensure its business success. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coordinate-based%20optimal%20routing" title="coordinate-based optimal routing">coordinate-based optimal routing</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamiltonian%20Circuit" title=" Hamiltonian Circuit"> Hamiltonian Circuit</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithm" title=" heuristic algorithm"> heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20routing%20problem" title=" vehicle routing problem"> vehicle routing problem</a> </p> <a href="https://publications.waset.org/abstracts/136218/a-coordinate-based-heuristic-route-search-algorithm-for-delivery-truck-routing-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136218.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">222</span> Algorithms for Run-Time Task Mapping in NoC-Based Heterogeneous MPSoCs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20Benhaoua">M. K. Benhaoua</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20Singh"> A. K. Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20E.%20Benyamina"> A. E. Benyamina</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Boulet"> P. Boulet</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mapping parallelized tasks of applications onto these MPSoCs can be done either at design time (static) or at run-time (dynamic). Static mapping strategies find the best placement of tasks at design-time, and hence, these are not suitable for dynamic workload and seem incapable of runtime resource management. The number of tasks or applications executing in MPSoC platform can exceed the available resources, requiring efficient run-time mapping strategies to meet these constraints. This paper describes a new Spiral Dynamic Task Mapping heuristic for mapping applications onto NoC-based Heterogeneous MPSoC. This heuristic is based on packing strategy and routing Algorithm proposed also in this paper. Heuristic try to map the tasks of an application in a clustering region to reduce the communication overhead between the communicating tasks. The heuristic proposed in this paper attempts to map the tasks of an application that are most related to each other in a spiral manner and to find the best possible path load that minimizes the communication overhead. In this context, we have realized a simulation environment for experimental evaluations to map applications with varying number of tasks onto an 8x8 NoC-based Heterogeneous MPSoCs platform, we demonstrate that the new mapping heuristics with the new modified dijkstra routing algorithm proposed are capable of reducing the total execution time and energy consumption of applications when compared to state-of-the-art run-time mapping heuristics reported in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiprocessor%20system%20on%20chip" title="multiprocessor system on chip">multiprocessor system on chip</a>, <a href="https://publications.waset.org/abstracts/search?q=MPSoC" title=" MPSoC"> MPSoC</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20on%20chip" title=" network on chip"> network on chip</a>, <a href="https://publications.waset.org/abstracts/search?q=NoC" title=" NoC"> NoC</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20architectures" title=" heterogeneous architectures"> heterogeneous architectures</a>, <a href="https://publications.waset.org/abstracts/search?q=run-time%20mapping%20heuristics" title=" run-time mapping heuristics"> run-time mapping heuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=routing%20algorithm" title=" routing algorithm "> routing algorithm </a> </p> <a href="https://publications.waset.org/abstracts/24295/algorithms-for-run-time-task-mapping-in-noc-based-heterogeneous-mpsocs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24295.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">489</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">221</span> Tabu Search Algorithm for Ship Routing and Scheduling Problem with Time Window</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Moh.%20Alhamad">Khaled Moh. Alhamad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a tabu search heuristic for a ship routing and scheduling problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heuristic" title="heuristic">heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=tabu%20search" title=" tabu search"> tabu search</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation" title=" transportation"> transportation</a> </p> <a href="https://publications.waset.org/abstracts/43484/tabu-search-algorithm-for-ship-routing-and-scheduling-problem-with-time-window" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43484.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">220</span> A New Heuristic Algorithm for Maximization Total Demands of Nodes and Number of Covered Nodes Simultaneously</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Saghehei">Ehsan Saghehei</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Eghbali"> Mahdi Eghbali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The maximal covering location problem (MCLP) was originally developed to determine a set of facility locations which would maximize the total customers' demand serviced by the facilities within a predetermined critical service criterion. However, on some problems that differences between the demand nodes are covered or the number of nodes each node is large, the method of solving MCLP may ignore these differences. In this paper, Heuristic solution based on the ranking of demands in each node and the number of nodes covered by each node according to a predetermined critical value is proposed. The output of this method is to maximize total demands of nodes and number of covered nodes, simultaneously. Furthermore, by providing an example, the solution algorithm is described and its results are compared with Greedy and Lagrange algorithms. Also, the results of the algorithm to solve the larger problem sizes that compared with other methods are provided. A summary and future works conclude the paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heuristic%20solution" title="heuristic solution">heuristic solution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximal%20covering%20location%20problem" title=" maximal covering location problem"> maximal covering location problem</a>, <a href="https://publications.waset.org/abstracts/search?q=ranking" title=" ranking"> ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=set%20covering" title=" set covering"> set covering</a> </p> <a href="https://publications.waset.org/abstracts/29314/a-new-heuristic-algorithm-for-maximization-total-demands-of-nodes-and-number-of-covered-nodes-simultaneously" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29314.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">573</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">219</span> A Low Power Consumption Routing Protocol Based on a Meta-Heuristics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaddi%20Mohammed">Kaddi Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Benahmed%20Khelifa%20D.%20Benatiallah"> Benahmed Khelifa D. Benatiallah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A sensor network consists of a large number of sensors deployed in areas to monitor and communicate with each other through a wireless medium. The collected routing data in the network consumes most of the energy of the sensor nodes. For this purpose, multiple routing approaches have been proposed to conserve energy resource at the sensors and to overcome the challenges of its limitation. In this work, we propose a new low energy consumption routing protocol for wireless sensor networks based on a meta-heuristic methods. Our protocol is to operate more fairly energy when routing captured data to the base station. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=WSN" title="WSN">WSN</a>, <a href="https://publications.waset.org/abstracts/search?q=routing" title=" routing"> routing</a>, <a href="https://publications.waset.org/abstracts/search?q=energy" title=" energy"> energy</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a> </p> <a href="https://publications.waset.org/abstracts/47922/a-low-power-consumption-routing-protocol-based-on-a-meta-heuristics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47922.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">343</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">218</span> New Approach for Minimizing Wavelength Fragmentation in Wavelength-Routed WDM Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sami%20Baraketi">Sami Baraketi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Marie%20Garcia"> Jean Marie Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Brun"> Olivier Brun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=WDM" title="WDM">WDM</a>, <a href="https://publications.waset.org/abstracts/search?q=lightpath" title=" lightpath"> lightpath</a>, <a href="https://publications.waset.org/abstracts/search?q=RWA" title=" RWA"> RWA</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelength%20fragmentation" title=" wavelength fragmentation"> wavelength fragmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming"> linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic" title=" heuristic"> heuristic</a> </p> <a href="https://publications.waset.org/abstracts/25101/new-approach-for-minimizing-wavelength-fragmentation-in-wavelength-routed-wdm-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25101.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">527</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">217</span> Joint Training Offer Selection and Course Timetabling Problems: Models and Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gianpaolo%20Ghiani">Gianpaolo Ghiani</a>, <a href="https://publications.waset.org/abstracts/search?q=Emanuela%20Guerriero"> Emanuela Guerriero</a>, <a href="https://publications.waset.org/abstracts/search?q=Emanuele%20Manni"> Emanuele Manni</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessandro%20Romano"> Alessandro Romano</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we deal with a variant of the classical course timetabling problem that has a practical application in many areas of education. In particular, in this paper we are interested in high schools remedial courses. The purpose of such courses is to provide under-prepared students with the skills necessary to succeed in their studies. In particular, a student might be under prepared in an entire course, or only in a part of it. The limited availability of funds, as well as the limited amount of time and teachers at disposal, often requires schools to choose which courses and/or which teaching units to activate. Thus, schools need to model the training offer and the related timetabling, with the goal of ensuring the highest possible teaching quality, by meeting the above-mentioned financial, time and resources constraints. Moreover, there are some prerequisites between the teaching units that must be satisfied. We first present a Mixed-Integer Programming (MIP) model to solve this problem to optimality. However, the presence of many peculiar constraints contributes inevitably in increasing the complexity of the mathematical model. Thus, solving it through a general purpose solver may be performed for small instances only, while solving real-life-sized instances of such model requires specific techniques or heuristic approaches. For this purpose, we also propose a heuristic approach, in which we make use of a fast constructive procedure to obtain a feasible solution. To assess our exact and heuristic approaches we perform extensive computational results on both real-life instances (obtained from a high school in Lecce, Italy) and randomly generated instances. Our tests show that the MIP model is never solved to optimality, with an average optimality gap of 57%. On the other hand, the heuristic algorithm is much faster (in about the 50% of the considered instances it converges in approximately half of the time limit) and in many cases allows achieving an improvement on the objective function value obtained by the MIP model. Such an improvement ranges between 18% and 66%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heuristic" title="heuristic">heuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=MIP%20model" title=" MIP model"> MIP model</a>, <a href="https://publications.waset.org/abstracts/search?q=remedial%20course" title=" remedial course"> remedial course</a>, <a href="https://publications.waset.org/abstracts/search?q=school" title=" school"> school</a>, <a href="https://publications.waset.org/abstracts/search?q=timetabling" title=" timetabling"> timetabling</a> </p> <a href="https://publications.waset.org/abstracts/29805/joint-training-offer-selection-and-course-timetabling-problems-models-and-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29805.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right 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