A Q-learning driven multi-objective evolutionary algorithm for worker fatigue dual-resource-constrained distributed hybrid flow shop

•A distributed hybrid flow shop considering worker fatigue is proposed.•A Q-learning driven multi-objective algorithm is designed.•A decoding heuristic that considers actual worker productivity is embedded.•The problem-based heuristic initialization methods are adopted.•New crossover and mutation op...

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Vydané v:	Computers & operations research Ročník 175; s. 106919
Hlavní autori:	Song, Haonan, Li, Junqing, Du, Zhaosheng, Yu, Xin, Xu, Ying, Zheng, Zhixin, Li, Jiake
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.03.2025
Predmet:	Distributed hybrid flow shop scheduling Dual-resource constraints Multi-objective evolutionary algorithm Q-learning Worker fatigue Dual-resource constraints Q-learning Worker fatigue Multi-objective evolutionary algorithm Distributed hybrid flow shop scheduling
ISSN:	0305-0548
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Shrnutí:	•A distributed hybrid flow shop considering worker fatigue is proposed.•A Q-learning driven multi-objective algorithm is designed.•A decoding heuristic that considers actual worker productivity is embedded.•The problem-based heuristic initialization methods are adopted.•New crossover and mutation operators are designed to find better feasible solutions. In practical industrial production, workers are often critical resources in manufacturing systems. However, few studies have considered the level of worker fatigue when assigning resources and arranging tasks, which has a negative impact on productivity. To fill this gap, the distributed hybrid flow shop scheduling problem with dual-resource constraints considering worker fatigue (DHFSPW) is introduced in this study. Due to the complexity and diversity of distributed manufacturing and multi-objective, a Q-learning driven multi-objective evolutionary algorithm (QMOEA) is proposed to optimize both the makespan and total energy consumption of the DHFSPW at the same time. In QMOEA, solutions are represented by a four-dimensional vector, and a decoding heuristic that accounts for real-time worker productivity is proposed. Additionally, three problem-specific initialization heuristics are developed to enhance convergence and diversity capabilities. Moreover, encoding-based crossover, mirror crossover and balanced mutation methods are presented to improve the algorithm’s exploitation capabilities. Furthermore, a Q-learning based local search is employed to explore promising nondominated solutions across different dimensions. Finally, the QMOEA is assessed using a set of randomly generated instances, and a detailed comparison with state-of-the-art algorithms is performed to demonstrate its efficiency and robustness.
ISSN:	0305-0548
DOI:	10.1016/j.cor.2024.106919