A HYBRID SIMULATED ANNEALING ALGORITHM FOR SOLVING MULTI-OBJECTIVE CONTAINER-LOADING PROBLEMS

In this article, we explored a new approach to solution of multi-objective container-loading problems mostly encountered in transportation and wholesaling industries. Our goal is to load the items (boxes) that would provide the highest total weight to the container in the best possible way. These tw...

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Bibliographic Details
Published inApplied artificial intelligence Vol. 24; no. 5; pp. 463 - 486
Main Authors Dereli, Türkay, Sena Das, Gülesin
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis Group 28.05.2010
Taylor & Francis Ltd
Subjects
Algorithms
Artificial intelligence
Benchmarks
Computer programming
Containers
Expert systems
Goal programming
Maximization
Simulated annealing
Simulation
Transportation
Validation studies
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Summary:In this article, we explored a new approach to solution of multi-objective container-loading problems mostly encountered in transportation and wholesaling industries. Our goal is to load the items (boxes) that would provide the highest total weight to the container in the best possible way. These two objectives (weight maximization and volume utilization) are conflicting because the volume of a box is usually not proportional to its weight. A weighted goal programming model is formulated and presented. A simulated annealing (SA) algorithm accompanied by a heuristic filling procedure is then proposed to solve the model. The proposed algorithm has been first tested on a set of benchmark problems available in the literature and then used for real-world data provided by a distribution company. The computational results have validated significance and usefulness of the proposed approach.
Bibliography:ObjectType-Article-2
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ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2010.481488