TY - JOUR
T1 - A Q-learning guided dual population genetic algorithm for distributed permutation flow shop scheduling problem with machine having fuzzy processing efficiency
AU - Zuo, Guanzhong
AU - Jia, Zhiyang
AU - Wu, Zongyang
AU - Shi, Jiawei
AU - Wang, Gang
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/8/1
Y1 - 2025/8/1
N2 - The emergence of Industry 5.0 has shifted the focus of research towards green manufacturing, flexible and digitalized production, particularly emphasizing human–machine collaboration and distributed manufacturing systems. Such a transition introduces increasingly complex and dynamic challenges for manufacturing enterprises, resulting in heightened uncertainties in production scheduling where traditional scheduling approaches often exhibit limited capability in handling multi-objective trade-offs. To overcome these limitations, a Q-learning guided dual-population genetic algorithm (QGGA) is proposed in the current study, featuring two key innovations: (1) a cooperation pool with dual-population knowledge sharing that stores non-dominated solutions from both populations while maintaining their evolutionary independence, (2) a state-dependent action adaptation mechanism that dynamically selects actions from nine heuristic rules using Q-learning. The cooperation pool enables synergistic optimization by storing non-dominated solutions from both populations to enable knowledge exchange while preserving their independent optimization processes. The Q-learning component continuously optimizes action selection based on solution diversity metric and convergence metric. Experimental results demonstrate that the proposed method achieves 19.1% improvement in Hypervolume (HV) and 65.5% reduction in inverted Generational Distance (IGD) compared to NSGA-II, outperforms PPO by 24.8% HV, and achieves an 90.4%better IGD than MOEA/D, achieving superior balance between solution robustness and computational efficiency. This advancement provides a new methodological framework for addressing Industry 5.0 scheduling challenges under uncertainty.
AB - The emergence of Industry 5.0 has shifted the focus of research towards green manufacturing, flexible and digitalized production, particularly emphasizing human–machine collaboration and distributed manufacturing systems. Such a transition introduces increasingly complex and dynamic challenges for manufacturing enterprises, resulting in heightened uncertainties in production scheduling where traditional scheduling approaches often exhibit limited capability in handling multi-objective trade-offs. To overcome these limitations, a Q-learning guided dual-population genetic algorithm (QGGA) is proposed in the current study, featuring two key innovations: (1) a cooperation pool with dual-population knowledge sharing that stores non-dominated solutions from both populations while maintaining their evolutionary independence, (2) a state-dependent action adaptation mechanism that dynamically selects actions from nine heuristic rules using Q-learning. The cooperation pool enables synergistic optimization by storing non-dominated solutions from both populations to enable knowledge exchange while preserving their independent optimization processes. The Q-learning component continuously optimizes action selection based on solution diversity metric and convergence metric. Experimental results demonstrate that the proposed method achieves 19.1% improvement in Hypervolume (HV) and 65.5% reduction in inverted Generational Distance (IGD) compared to NSGA-II, outperforms PPO by 24.8% HV, and achieves an 90.4%better IGD than MOEA/D, achieving superior balance between solution robustness and computational efficiency. This advancement provides a new methodological framework for addressing Industry 5.0 scheduling challenges under uncertainty.
KW - Distributed flow shop scheduling problem
KW - Fuzzy processing efficiency
KW - Genetic algorithm
KW - Q-learning
UR - http://www.scopus.com/inward/record.url?scp=105004648144&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2025.127882
DO - 10.1016/j.eswa.2025.127882
M3 - Article
AN - SCOPUS:105004648144
SN - 0957-4174
VL - 285
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 127882
ER -