Runtime Policy Orchestration for Autonomous Industrial Control and Smart Manufacturing Systems: A Unified Framework for Governance, Compliance, and Adaptive Resilience

Abstract

The rapid evolution of Industry 4.0 and emerging Industry 5.0 paradigms has accelerated the adoption of autonomous cyber-physical systems (CPS) in smart manufacturing, creating a need for robust runtime policy orchestration to ensure governance, regulatory compliance, and resilience under dynamic disruptions and cyber threats. This study addresses the lack of unified frameworks by proposing the Policy Resilient Orchestrator (PRO), a simulation-driven architecture integrating machine-readable policy cards, multi-agent reinforcement learning using Proximal Policy Optimization (PPO), multi-objective optimization via NSGA-II, runtime verification, and digital twin-based validation. A quantitative, desk-based methodology utilizing synthetic and benchmark datasets was employed to model CPS environments without physical hardware. NSGA-II generated Pareto-optimal policy configurations, while PPO enabled adaptive runtime control supported by simplex switching for resilience. Monte Carlo simulations across normal, attack, and failure scenarios showed significant improvements, with Governance Efficiency (0.961), Resilience Index (0.993), and Downtime Reduction (0.988), all statistically outperforming baselines (p < 0.0001). PRO demonstrates a scalable, reliable solution for trustworthy smart manufacturing systems.