Dynamic distributed production scheduling problem with simultaneous job arrivals and machine breakdowns: Learning to learn iterated greedy algorithm

Dynamic distributed production scheduling poses significant challenges to modern manufacturing systems due to environmental complexity and the need to respond to real-time disruptions. This paper studies the dynamic distributed hybrid flow shop problem (DDHFSP) with simultaneous dynamic job arrivals and machine breakdowns, aiming to minimize makespan. To address this problem, a learnable iterated greedy algorithm (LIG) is proposed within the classical IG framework. LIG is designed to learn to learn​ how to adaptively configure its operational components by extracting knowledge from search trajectories, thereby effectively handling dynamic scheduling environments. Based on the IG structure, four rescheduling strategies for dynamic events, four destruction-reconstruction strategies, and four local search strategies are designed. The algorithm employs a long short-term memory (LSTM) network to extract temporally dependent production features, and adopts proximal policy optimization (PPO) to build an agent that adaptively selects the most promising combination of strategies according to the current state for execution within the IG framework. Experimental results demonstrate that LIG outperforms several state-of-the-art metaheuristic and reinforcement learning-based methods across various problem scales and dynamic scenarios, exhibiting faster convergence, better solution quality, higher stability, and stronger generalization capability. Ultimately, the proposed learnable mechanism establishes a new paradigm for tackling dynamic disturbances in DDHFSP.