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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

Hongliang Zhang1,2, Yi Chen1, Yuteng Zhang1, Gongjie Xu3,*

1 School of Management Science and Engineering, Anhui University of Technology, Ma’anshan, 243032, China
2 Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma’anshan, 243032, China
3 Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

* Corresponding Author: Gongjie Xu. Email: email

Computer Modeling in Engineering & Sciences 2024, 140(2), 1459-1483. https://doi.org/10.32604/cmes.2024.049756

Abstract

The distributed flexible job shop scheduling problem (DFJSP) has attracted great attention with the growth of the global manufacturing industry. General DFJSP research only considers machine constraints and ignores worker constraints. As one critical factor of production, effective utilization of worker resources can increase productivity. Meanwhile, energy consumption is a growing concern due to the increasingly serious environmental issues. Therefore, the distributed flexible job shop scheduling problem with dual resource constraints (DFJSP-DRC) for minimizing makespan and total energy consumption is studied in this paper. To solve the problem, we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer (Q-MOGWO). In Q-MOGWO, high-quality initial solutions are generated by a hybrid initialization strategy, and an improved active decoding strategy is designed to obtain the scheduling schemes. To further enhance the local search capability and expand the solution space, two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed. These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions. The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances. The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

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APA Style
Zhang, H., Chen, Y., Zhang, Y., Xu, G. (2024). Energy-saving distributed flexible job shop scheduling optimization with dual resource constraints based on integrated q-learning multi-objective grey wolf optimizer. Computer Modeling in Engineering & Sciences, 140(2), 1459-1483. https://doi.org/10.32604/cmes.2024.049756
Vancouver Style
Zhang H, Chen Y, Zhang Y, Xu G. Energy-saving distributed flexible job shop scheduling optimization with dual resource constraints based on integrated q-learning multi-objective grey wolf optimizer. Comput Model Eng Sci. 2024;140(2):1459-1483 https://doi.org/10.32604/cmes.2024.049756
IEEE Style
H. Zhang, Y. Chen, Y. Zhang, and G. Xu "Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer," Comput. Model. Eng. Sci., vol. 140, no. 2, pp. 1459-1483. 2024. https://doi.org/10.32604/cmes.2024.049756



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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