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<p>Human-centric manufacturing toward Industry 5.0 requires modeling how operator behavior and supervisory governance co-evolve within sociotechnical production environments. This study investigates supervision–compliance synergy (SCS) by formulating the interactions between operators and engineers as an evolutionary decision process under bounded rationality and perception-driven uncertainty. We propose a fuzzy evolutionary game (FEZ) framework that extends an evolutionary cooperation–competition game to multi-machine settings, incorporating cross-machine spillover effects and fuzzy payoff structures to represent subjective evaluations of cost, risk, and reward. Uncertainty-aware validation is conducted using a fuzzy Moran-process-based stochastic simulation, and supervisory control parameters are tuned through optimization. A connector-assembly case study shows that, without appropriate intervention, strategy evolution can converge to unstable behavioral outcomes, while parameter tuning improves convergence toward higher compliance; however, sustained improvement may require complementary measures such as retraining or interface redesign. The proposed framework provides a theoretically grounded and practically applicable approach for analyzing and tuning dynamic supervision strategies to promote stable compliance and operational resilience in Industry 5.0 manufacturing systems.</p>

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Sociotechnical system dynamics in human-centric industry 5.0 manufacturing: Fuzzy evolutionary game modeling and analysis of supervision–compliance synergy

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Mulang Song,
  • Xuejian Gong,
  • Roger J. Jiao

摘要

Human-centric manufacturing toward Industry 5.0 requires modeling how operator behavior and supervisory governance co-evolve within sociotechnical production environments. This study investigates supervision–compliance synergy (SCS) by formulating the interactions between operators and engineers as an evolutionary decision process under bounded rationality and perception-driven uncertainty. We propose a fuzzy evolutionary game (FEZ) framework that extends an evolutionary cooperation–competition game to multi-machine settings, incorporating cross-machine spillover effects and fuzzy payoff structures to represent subjective evaluations of cost, risk, and reward. Uncertainty-aware validation is conducted using a fuzzy Moran-process-based stochastic simulation, and supervisory control parameters are tuned through optimization. A connector-assembly case study shows that, without appropriate intervention, strategy evolution can converge to unstable behavioral outcomes, while parameter tuning improves convergence toward higher compliance; however, sustained improvement may require complementary measures such as retraining or interface redesign. The proposed framework provides a theoretically grounded and practically applicable approach for analyzing and tuning dynamic supervision strategies to promote stable compliance and operational resilience in Industry 5.0 manufacturing systems.