MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation
In the real manufacturing environment, the machining stage of the jobs and the assembly stage of the products are often completed in different workshops. In addition, automatic guided vehicle (AGV) plays an indispensable role in the transportation of jobs from machining workshop to assembly workshop...
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2025-01-01
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| author | Shiming Yang Leilei Meng Saif Ullah Biao Zhang Hongyan Sang Peng Duan |
| author_facet | Shiming Yang Leilei Meng Saif Ullah Biao Zhang Hongyan Sang Peng Duan |
| author_sort | Shiming Yang |
| collection | DOAJ |
| description | In the real manufacturing environment, the machining stage of the jobs and the assembly stage of the products are often completed in different workshops. In addition, automatic guided vehicle (AGV) plays an indispensable role in the transportation of jobs from machining workshop to assembly workshop. This paper studies multi-objective three-stage flexible job shop scheduling problem (FJSP-T-A) with minimizing both the makespan and the total energy consumption. In FJSP-T-A, jobs are first machined in flexible job shop, then are transported to assembly workshop by AGVs, and finally are assembled in assembly workshop. To solve this problem, a mixed-integer linear programming model (MILP) is developed and the optimal Pareto front for small-scale instances are solved by using the <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-method. FJSP-T-A is NP-hard, and an efficient multi-population co-evolutionary algorithm (MPCEA) is proposed to efficiently solve large-scale instances. In the MPCEA, we design a strategy to select relatively high-quality individuals to enhance the algorithm’s convergence speed, and design a multi-objective variable-neighborhood search (MOVNS) method to improve the local search ability. Experiments are conducted to prove the effectiveness of the MILP model and the MPCEA. |
| format | Article |
| id | doaj-art-bbfde9204d7e419b9510a43e0f5dae7d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-bbfde9204d7e419b9510a43e0f5dae7d2025-02-12T00:01:30ZengIEEEIEEE Access2169-35362025-01-0113253692538610.1109/ACCESS.2025.353582510856114MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV TransportationShiming Yang0Leilei Meng1https://orcid.org/0000-0003-1439-4832Saif Ullah2https://orcid.org/0000-0003-4772-7760Biao Zhang3https://orcid.org/0000-0003-4148-8172Hongyan Sang4https://orcid.org/0000-0001-7476-5039Peng Duan5https://orcid.org/0000-0002-7396-7592School of Computer Science, Liaocheng University, Liaocheng, ChinaSchool of Computer Science, Liaocheng University, Liaocheng, ChinaDepartment of Industrial Engineering, University of Engineering and Technology, Taxila, PakistanSchool of Computer Science, Liaocheng University, Liaocheng, ChinaSchool of Computer Science, Liaocheng University, Liaocheng, ChinaSchool of Computer Science, Liaocheng University, Liaocheng, ChinaIn the real manufacturing environment, the machining stage of the jobs and the assembly stage of the products are often completed in different workshops. In addition, automatic guided vehicle (AGV) plays an indispensable role in the transportation of jobs from machining workshop to assembly workshop. This paper studies multi-objective three-stage flexible job shop scheduling problem (FJSP-T-A) with minimizing both the makespan and the total energy consumption. In FJSP-T-A, jobs are first machined in flexible job shop, then are transported to assembly workshop by AGVs, and finally are assembled in assembly workshop. To solve this problem, a mixed-integer linear programming model (MILP) is developed and the optimal Pareto front for small-scale instances are solved by using the <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>-method. FJSP-T-A is NP-hard, and an efficient multi-population co-evolutionary algorithm (MPCEA) is proposed to efficiently solve large-scale instances. In the MPCEA, we design a strategy to select relatively high-quality individuals to enhance the algorithm’s convergence speed, and design a multi-objective variable-neighborhood search (MOVNS) method to improve the local search ability. Experiments are conducted to prove the effectiveness of the MILP model and the MPCEA.https://ieeexplore.ieee.org/document/10856114/Three-stage flexible job shop scheduling problemmixed-integer linear programmingco-evolutionary algorithmvariable-neighborhood search |
| spellingShingle | Shiming Yang Leilei Meng Saif Ullah Biao Zhang Hongyan Sang Peng Duan MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation IEEE Access Three-stage flexible job shop scheduling problem mixed-integer linear programming co-evolutionary algorithm variable-neighborhood search |
| title | MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation |
| title_full | MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation |
| title_fullStr | MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation |
| title_full_unstemmed | MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation |
| title_short | MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation |
| title_sort | milp modeling and optimization of multi objective three stage flexible job shop scheduling problem with assembly and agv transportation |
| topic | Three-stage flexible job shop scheduling problem mixed-integer linear programming co-evolutionary algorithm variable-neighborhood search |
| url | https://ieeexplore.ieee.org/document/10856114/ |
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