Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm
With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2586 |
| _version_ | 1827307157376204800 |
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| author | Jili Kong Zhen Wang |
| author_facet | Jili Kong Zhen Wang |
| author_sort | Jili Kong |
| collection | DOAJ |
| description | With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time. |
| first_indexed | 2024-04-24T18:34:28Z |
| format | Article |
| id | doaj.art-69cda96b59e14a1f990e2cebcc530ad5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:34:28Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-69cda96b59e14a1f990e2cebcc530ad52024-03-27T13:20:10ZengMDPI AGApplied Sciences2076-34172024-03-01146258610.3390/app14062586Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm AlgorithmJili Kong0Zhen Wang1School of Modern Post, Beijing University of Posts and Telecommunications, Haidian District, Beijing 100876, ChinaSchool of Modern Post, Beijing University of Posts and Telecommunications, Haidian District, Beijing 100876, ChinaWith the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time.https://www.mdpi.com/2076-3417/14/6/2586multi-equipment work centerflexible job shop scheduling problemsetup timehandling timemulti-objective optimizationimproved discrete particle swarm optimization |
| spellingShingle | Jili Kong Zhen Wang Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm Applied Sciences multi-equipment work center flexible job shop scheduling problem setup time handling time multi-objective optimization improved discrete particle swarm optimization |
| title | Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm |
| title_full | Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm |
| title_fullStr | Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm |
| title_full_unstemmed | Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm |
| title_short | Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm |
| title_sort | research on flexible job shop scheduling problem with handling and setup time based on improved discrete particle swarm algorithm |
| topic | multi-equipment work center flexible job shop scheduling problem setup time handling time multi-objective optimization improved discrete particle swarm optimization |
| url | https://www.mdpi.com/2076-3417/14/6/2586 |
| work_keys_str_mv | AT jilikong researchonflexiblejobshopschedulingproblemwithhandlingandsetuptimebasedonimproveddiscreteparticleswarmalgorithm AT zhenwang researchonflexiblejobshopschedulingproblemwithhandlingandsetuptimebasedonimproveddiscreteparticleswarmalgorithm |