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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| Published in: | Computers & industrial engineering Vol. 188; p. 109903 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.02.2024
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| Subjects: | |
| ISSN: | 0360-8352, 1879-0550 |
| Online Access: | Get full text |
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| Summary: | •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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| ISSN: | 0360-8352 1879-0550 |
| DOI: | 10.1016/j.cie.2024.109903 |