A Mathematical Model and Self-Adaptive NSGA-II for a Multiobjective IPPS Problem Subject to Delivery Time

• Published in: Mathematical problems in engineering Vol. 2020; no. 2020; pp. 1 - 12
• Main Authors: Li, Yan, Han, Zhoupeng, Liu, Yong, Gao, Xinqin, Yang, Mingshun, Ba, Li, Xu, Erbao
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Adaptive algorithms; Algorithms; Breakdowns; Delivery scheduling; Energy conservation; Energy consumption; Genetic algorithms; Job shops; Linear programming; Literature reviews; Manufacturing; Mathematical models; Objectives; Process planning; Researchers; Scheduling; Sorting algorithms; Workload; Workloads
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2020/6012737

Process planning and scheduling are two important components of manufacturing systems. This paper deals with a multiobjective just-in-time integrated process planning and scheduling (MOJIT-IPPS) problem. Delivery time and machine workload are considered to make IPPS problem more suitable for manufacturing environments. The earliness/tardiness penalty, maximum machine workload, and total machine workload are objectives that are minimized. The decoding method is a crucial part that significantly influences the scheduling results. We develop a self-adaptive decoding method to obtain better results. A nondominated sorting genetic algorithm with self-adaptive decoding (SD-NSGA-II) is proposed for solving MOJIT-IPPS. Finally, the model and algorithm are proven through an example. Furthermore, different scale examples are tested to prove the good performance of the proposed method.