Learning-Assisted Genetic Programming Hyperheuristic for Dynamic Distributed Hybrid Flow Shop Scheduling With Uncertain Events
针对分布式混合流水车间中机器故障和工件恶化等不确定事件，提出一种学习辅助的遗传编程超启发式算法，以最小化最大完工时间，实验表明其优于传统方法。
Distributed hybrid flow shop scheduling is prevalent in industries such as integrated circuit manufacturing, ceramic frit production, glass fiber processing, and steelmaking. Machine breakdowns and deteriorating jobs represent common and disruptive sources of uncertainty in these distributed manufacturing environments. However, existing research has often overlooked these significant challenges. To address this gap, this article addresses the dynamic distributed hybrid flow shop scheduling problem with machine breakdowns and deteriorating jobs (DHFSP-MBDJs), and develops the mathematical model. We propose a learning-assisted genetic programming hyperheuristic (L-GP-HH) algorithm to minimize makespan. L-GP-HH incorporates a novel constructive heuristic for factory assignment and develops specific terminal sets based on fundamental factors and uncertain events to generate genetic programming (GP) heuristics. Additionally, we establish a learning probability model to optimize the selection of GP-generated rules during the solution process. Extensive numerical experiments demonstrate that L-GP-HH consistently outperforms conventional GP hyperheuristics (GP-HHs), benchmark scheduling rules, and four efficient meta-and hyperheuristics. It exhibits superior flexibility and adaptability in handling complex scheduling under dynamic environment with uncertainties. This study provides critical insights for practitioners, emphasizing the necessity of concurrently considering machine-and job-related uncertainties in dynamic distributed manufacturing systems to enhance scheduling robustness and operational efficiency.