Multi-resource constrained flexible job shop scheduling problem with fixture-pallet combinatorial optimisation
•Flexible job shop scheduling with machine-fixture-pallet constraints is first proposed.•Scheduling optimisation and fixture-pallet combinatorial optimisation are considered simultaneously.•Feasibilty correction strategy is proposed to solve potential conflict of machine selection and fixture select...
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| Veröffentlicht in: | Computers & industrial engineering Jg. 188; S. 109903 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.02.2024
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| ISSN: | 0360-8352, 1879-0550 |
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| Abstract | •Flexible job shop scheduling with machine-fixture-pallet constraints is first proposed.•Scheduling optimisation and fixture-pallet combinatorial optimisation are considered simultaneously.•Feasibilty correction strategy is proposed to solve potential conflict of machine selection and fixture selection.•Self-learning variable neighbourhood search is introduced to further improve algorithm performance.•Test cases from real production scenarios are utilised to prove the advantage of our proposed algorithm.
There is a lack of research on the flexible job shop scheduling problem (FJSP) considering limited fixture-pallet resources in multi-product mixed manufacturing workshops. However, field research in a leading engine manufacturer in China has revealed that fixture-pallet resources are a key factor limiting capacity breakthroughs although they play an auxiliary role in the production process. Thus, in this paper, we propose a methodology for the multi-resource constrained flexible job shop scheduling problem with fixture-pallet combinatorial optimisation. First, a mixed integer programming model with machine-fixture-pallet constraints is constructed aiming to minimize makespan. Then, a novel genetic algorithm integrated with feasibility correction strategy and self-learning variable neighbourhood search (VNS) is proposed to address the complicated scheduling problem, where the feasibility correction strategy is designed to solve potential conflict between machine selection and fixture selection chromosomes and self-learning VNS is executed to further improve the optimisation capability. Moreover, the effectiveness and efficiency of proposed algorithm are demonstrated by computational experiments with real data from cooperated engine manufacturing plant, which would provide convincing support for real production scheduling under complex scenarios. |
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| AbstractList | •Flexible job shop scheduling with machine-fixture-pallet constraints is first proposed.•Scheduling optimisation and fixture-pallet combinatorial optimisation are considered simultaneously.•Feasibilty correction strategy is proposed to solve potential conflict of machine selection and fixture selection.•Self-learning variable neighbourhood search is introduced to further improve algorithm performance.•Test cases from real production scenarios are utilised to prove the advantage of our proposed algorithm.
There is a lack of research on the flexible job shop scheduling problem (FJSP) considering limited fixture-pallet resources in multi-product mixed manufacturing workshops. However, field research in a leading engine manufacturer in China has revealed that fixture-pallet resources are a key factor limiting capacity breakthroughs although they play an auxiliary role in the production process. Thus, in this paper, we propose a methodology for the multi-resource constrained flexible job shop scheduling problem with fixture-pallet combinatorial optimisation. First, a mixed integer programming model with machine-fixture-pallet constraints is constructed aiming to minimize makespan. Then, a novel genetic algorithm integrated with feasibility correction strategy and self-learning variable neighbourhood search (VNS) is proposed to address the complicated scheduling problem, where the feasibility correction strategy is designed to solve potential conflict between machine selection and fixture selection chromosomes and self-learning VNS is executed to further improve the optimisation capability. Moreover, the effectiveness and efficiency of proposed algorithm are demonstrated by computational experiments with real data from cooperated engine manufacturing plant, which would provide convincing support for real production scheduling under complex scenarios. |
| ArticleNumber | 109903 |
| Author | Zhou, Yulu Du, Shichang Shen, Xiaoxiao Liu, Molin Lv, Jun Deng, Yafei |
| Author_xml | – sequence: 1 givenname: Molin surname: Liu fullname: Liu, Molin email: toujours.molin@sjtu.edu.cn organization: Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China – sequence: 2 givenname: Jun surname: Lv fullname: Lv, Jun email: jlv@dbm.ecnu.edu.cn organization: Faculty of Economics and Management, East China Normal University, Shanghai 200241, People’s Republic of China – sequence: 3 givenname: Shichang surname: Du fullname: Du, Shichang email: lovbin@sjtu.edu.cn organization: Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China – sequence: 4 givenname: Yafei surname: Deng fullname: Deng, Yafei email: phoenixdyf@sjtu.edu.cn organization: Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China – sequence: 5 givenname: Xiaoxiao orcidid: 0000-0003-1396-8451 surname: Shen fullname: Shen, Xiaoxiao email: sjtusxx98@sjtu.edu.cn organization: Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China – sequence: 6 givenname: Yulu surname: Zhou fullname: Zhou, Yulu email: yuluzhou@sjtu.edu.cn organization: Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China |
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