A Hyper-Heuristic for Dynamic Integrated Process Planning and Scheduling Problem With Reconfigurable Manufacturing Cells
Haoxin Guo, Jianhua Liu, Cunbo Zhuang, Hongliang Dong, Feng Zhang
Abstract
Manufacturing scheduling research has often overlooked the complexities of dynamic product assembly and testing scenarios, particularly those involving reconfigurable manufacturing cells (RMCs) and the integration of process planning and scheduling. This article addresses the problem of Dynamic Integrated Process Planning and Scheduling with RMCs, a novel and complex challenge in modern manufacturing systems. A variable-fidelity surrogate-assisted hyper-heuristic algorithm is proposed, which strategically integrates process planning and scheduling tasks to reduce computation time while improving solution quality. Unlike existing methods, our approach uses surrogate models to approximate expensive evaluations, significantly enhancing computational efficiency. In experiments, our method outperformed the second-best approach by 42.4% and the least effective method by 56.6% in terms of computational efficiency, demonstrating its capability to manage dynamic scheduling and cell reconfiguration challenges in large-scale, real-world manufacturing environments.