Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm
The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is...
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my.utm.594792021-12-15T07:34:32Z http://eprints.utm.my/id/eprint/59479/ Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm Piroozfard, Hamed Wong, Kuan Yew TJ Mechanical engineering and machinery The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm. 2015 Conference or Workshop Item PeerReviewed Piroozfard, Hamed and Wong, Kuan Yew (2015) Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm. In: International Conference on Mathematics, Engineering and Industrial Applications 2014 (ICoMEIA 2014), 28–30 May 2014, Penang, Malaysia. http://dx.doi.org/10.1063/1.4915695 |
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TJ Mechanical engineering and machinery Piroozfard, Hamed Wong, Kuan Yew Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm |
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The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm. |
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Conference or Workshop Item |
author |
Piroozfard, Hamed Wong, Kuan Yew |
author_facet |
Piroozfard, Hamed Wong, Kuan Yew |
author_sort |
Piroozfard, Hamed |
title |
Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm |
title_short |
Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm |
title_full |
Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm |
title_fullStr |
Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm |
title_full_unstemmed |
Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm |
title_sort |
solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm |
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2015 |
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http://eprints.utm.my/id/eprint/59479/ http://dx.doi.org/10.1063/1.4915695 |
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1720436912008724480 |
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