A Q-learning guided dual population genetic algorithm for distributed permutation flow shop scheduling problem with machine having fuzzy processing efficiency

Guanzhong Zuo, Zhiyang Jia*, Zongyang Wu, Jiawei Shi, Gang Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number127882
JournalExpert Systems with Applications
Volume285
DOIs
Publication statusPublished - 1 Aug 2025
Externally publishedYes

Keywords

  • Distributed flow shop scheduling problem
  • Fuzzy processing efficiency
  • Genetic algorithm
  • Q-learning

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