Risk-Aware Predictive–Proactive Sustainable Scheduling for Dynamic Flexible Job Shops Under Carbon and Labor Uncertainty

Abstract

Dynamic flexible job shop scheduling (DFJSP) faces compound uncertainty from job arrivals, machine breakdowns, labor volatility, and fluctuating carbon signals, and reactive or expectation-based methods cannot prevent costly tail events. This study proposes a five-stage risk-aware predictive–proactive framework that combines rolling-horizon forecasting, Conditional Value at Risk (CVaR) assessment, proactive pre-allocation of time buffers, preventive maintenance, and energy flexibility, risk-adaptive MOEA/D, and real-time control by a graph-attention reinforcement learning (RL) agent. MOEA/D and the agent exchange knowledge bidirectionally: RL trajectories seed the evolutionary search, and non-dominated solutions regularize policy learning. Over 30 independent runs under high compound uncertainty, the framework reduces delay CVaR by 65.4%, carbon emissions by 24.5%, and labor-shortage CVaR by 65.4% relative to NSGA-II, and by 49.3%, 9.8%, and 49.3% relative to single-agent deep RL, the strongest baseline, while keeping online decision latency below one second at the largest scale tested.