A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem
Most existing distributed hybrid flow-shop scheduling problems (DHFSPs) assume identical shops and lack consideration of heterogeneous shops. This study focuses on energy-efficient heterogeneous DHFSP. A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local sea...
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| Vydané v: | Expert systems with applications Ročník 237; s. 121570 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.03.2024
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | Most existing distributed hybrid flow-shop scheduling problems (DHFSPs) assume identical shops and lack consideration of heterogeneous shops. This study focuses on energy-efficient heterogeneous DHFSP. A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search is proposed in order to optimize both makespan and total energy consumption. Particle swarm optimization with multi-group is specifically designed as a global search strategy to improve the fast convergence performance of solutions in multi-direction of Pareto front. To improve the problem-specific knowledge search, two local search strategies are designed to further improve the quality and diversity of solutions. In addition, Q-learning is utilized to guide variable neighborhood search to better balance the exploration and exploitation of algorithms. This study investigates the effect of parameter setting and conducts extensive numerical tests. The comparative results and statistical analysis demonstrate the superior convergence and distribution performance of the proposed algorithm.
•Multi-group PSO as global search enhances multi-direction convergence of PF.•Two local search strategies cooperate with particle swarm optimization.•Inter-factory local search with insert and swap between critical factories.•Intra-factory local search with Q-learning and VNS within factories.•Two initialization methods increase the diversity of population. |
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| AbstractList | Most existing distributed hybrid flow-shop scheduling problems (DHFSPs) assume identical shops and lack consideration of heterogeneous shops. This study focuses on energy-efficient heterogeneous DHFSP. A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search is proposed in order to optimize both makespan and total energy consumption. Particle swarm optimization with multi-group is specifically designed as a global search strategy to improve the fast convergence performance of solutions in multi-direction of Pareto front. To improve the problem-specific knowledge search, two local search strategies are designed to further improve the quality and diversity of solutions. In addition, Q-learning is utilized to guide variable neighborhood search to better balance the exploration and exploitation of algorithms. This study investigates the effect of parameter setting and conducts extensive numerical tests. The comparative results and statistical analysis demonstrate the superior convergence and distribution performance of the proposed algorithm.
•Multi-group PSO as global search enhances multi-direction convergence of PF.•Two local search strategies cooperate with particle swarm optimization.•Inter-factory local search with insert and swap between critical factories.•Intra-factory local search with Q-learning and VNS within factories.•Two initialization methods increase the diversity of population. |
| ArticleNumber | 121570 |
| Author | Li, Chen Zhang, Wenqiang Zhang, Guohui Gen, Mitsuo Yang, Weidong |
| Author_xml | – sequence: 1 givenname: Wenqiang orcidid: 0000-0002-8214-0693 surname: Zhang fullname: Zhang, Wenqiang email: zhangwq@haut.edu.cn organization: College of Information Science and Engineering, Henan University of Technology, China – sequence: 2 givenname: Chen surname: Li fullname: Li, Chen email: lichen_haut@163.com organization: College of Information Science and Engineering, Henan University of Technology, China – sequence: 3 givenname: Mitsuo orcidid: 0000-0002-3670-1357 surname: Gen fullname: Gen, Mitsuo email: gen@flsi.or.jp organization: Fuzzy Logic Systems Institute/Tokyo University of Science, Japan – sequence: 4 givenname: Weidong surname: Yang fullname: Yang, Weidong email: mengguyang@163.com organization: Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, China – sequence: 5 givenname: Guohui orcidid: 0000-0001-9143-2922 surname: Zhang fullname: Zhang, Guohui email: zgh_hust@qq.com organization: School of Management Engineering, Zhengzhou University of Aeronautics, China |
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| Keywords | Memetic algorithm Q-learning Energy-efficient Particle swarm optimization Hybrid flow-shop scheduling Heterogeneous distributed scheduling |
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| SubjectTerms | Energy-efficient Heterogeneous distributed scheduling Hybrid flow-shop scheduling Memetic algorithm Particle swarm optimization Q-learning |
| Title | A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem |
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