Deep reinforcement learning for event-driven predictive–reactive multi-objective scheduling in dynamic flexible job shop

Abstract

In the era of intelligent manufacturing, dynamic scheduling problems characterized by high variability and complexity mandate prompt solutions. Deep Reinforcement Learning (DRL) is particularly effective for addressing these challenges through their formulation as Markov Decision Processes (MDPs). However, most DRL-based dynamic scheduling methods primarily adopt reactive strategies, thereby restricting their capacity to achieve global optimality. This paper addresses the Dynamic Multi-Objective Flexible Job Shop Scheduling Problem (DMFJSP) by introducing a comprehensive event-driven predictive–reactive scheduling framework designed to balance computational efficiency and solution quality. The approach employs a deterministic scheduling model that integrates the proposed Multi-Objective Hybrid Deep Q-Network (MOHDQN) as both the schedule generator and the baseline, enabling adaptive adjustment of objective weights. The Markov Jump Decision Process (MJDP) is incorporated to better manage event-driven predictive–reactive scheduling. Furthermore, seven rescheduling rules are developed to update schedules through a hybrid deep recurrent Q-network in response to dynamic events. Extensive numerical experiments across diverse problem scales demonstrate that the proposed method achieves a favorable balance between scheduling performance and computational efficiency. Finally, the most suitable rescheduling strategies for different enterprises are analyzed with respect to their specific operational complexities, and future development directions are also discussed.