Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm
The classical hybrid flow shop scheduling problem (HFSP) only treats machines as the only resource constraint, ignoring the dominant role of workers in production and manufacturing. Considering the dual flexibility of machine and worker, this paper studies the multi-objective hybrid flow shop schedu...
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9107143/ |
| _version_ | 1818433259746361344 |
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| author | Kaifeng Geng Chunming Ye Li Liu |
| author_facet | Kaifeng Geng Chunming Ye Li Liu |
| author_sort | Kaifeng Geng |
| collection | DOAJ |
| description | The classical hybrid flow shop scheduling problem (HFSP) only treats machines as the only resource constraint, ignoring the dominant role of workers in production and manufacturing. Considering the dual flexibility of machine and worker, this paper studies the multi-objective hybrid flow shop scheduling problem with dual resource constraints (DHFSP). Firstly, according to the characteristics of DHFSP and various constraints, the model is built to minimize the maximum completion time (makespan), total tardiness time and workload balance of worker. Then, an improved multi-objective memetic algorithm (IMOMA) is proposed to solve the DHFSP, which mainly includes the improvement of initial population, crossover, mutation and local search. In addition, Taguchi method is used to set parameters. Finally, through numerical experiments, IMOMA is compared with NSGA-II, MODE and MOMVO algorithms. The experimental results show that IMOMA can solve the multi-objective hybrid flow shop scheduling problem with dual resource constraints effectively. In terms of convergence, diversity and dominance of non-dominated solutions, IMOMA is significantly superior to other algorithms, but the distribution uniformity of non-dominated solutions of the four algorithms are not significantly different. |
| first_indexed | 2024-12-14T16:18:15Z |
| format | Article |
| id | doaj.art-c8f631b42c784adeb653117bca89fbc7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:18:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c8f631b42c784adeb653117bca89fbc72022-12-21T22:54:52ZengIEEEIEEE Access2169-35362020-01-01810452710454210.1109/ACCESS.2020.29996809107143Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic AlgorithmKaifeng Geng0https://orcid.org/0000-0001-5524-4713Chunming Ye1https://orcid.org/0000-0002-4090-5213Li Liu2https://orcid.org/0000-0001-8548-7329School of Business, University of Shanghai for Science and Technology, Shanghai, ChinaSchool of Business, University of Shanghai for Science and Technology, Shanghai, ChinaInformation Construction and Management Center, Nanyang Institute of Technology, Nanyang, ChinaThe classical hybrid flow shop scheduling problem (HFSP) only treats machines as the only resource constraint, ignoring the dominant role of workers in production and manufacturing. Considering the dual flexibility of machine and worker, this paper studies the multi-objective hybrid flow shop scheduling problem with dual resource constraints (DHFSP). Firstly, according to the characteristics of DHFSP and various constraints, the model is built to minimize the maximum completion time (makespan), total tardiness time and workload balance of worker. Then, an improved multi-objective memetic algorithm (IMOMA) is proposed to solve the DHFSP, which mainly includes the improvement of initial population, crossover, mutation and local search. In addition, Taguchi method is used to set parameters. Finally, through numerical experiments, IMOMA is compared with NSGA-II, MODE and MOMVO algorithms. The experimental results show that IMOMA can solve the multi-objective hybrid flow shop scheduling problem with dual resource constraints effectively. In terms of convergence, diversity and dominance of non-dominated solutions, IMOMA is significantly superior to other algorithms, but the distribution uniformity of non-dominated solutions of the four algorithms are not significantly different.https://ieeexplore.ieee.org/document/9107143/Hybrid flow shop schedulingdual resource constraintsmulti-objective optimizationmemetic algorithmTaguchi method |
| spellingShingle | Kaifeng Geng Chunming Ye Li Liu Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm IEEE Access Hybrid flow shop scheduling dual resource constraints multi-objective optimization memetic algorithm Taguchi method |
| title | Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm |
| title_full | Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm |
| title_fullStr | Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm |
| title_full_unstemmed | Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm |
| title_short | Research on Multi-Objective Hybrid Flow Shop Scheduling Problem With Dual Resource Constraints Using Improved Memetic Algorithm |
| title_sort | research on multi objective hybrid flow shop scheduling problem with dual resource constraints using improved memetic algorithm |
| topic | Hybrid flow shop scheduling dual resource constraints multi-objective optimization memetic algorithm Taguchi method |
| url | https://ieeexplore.ieee.org/document/9107143/ |
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