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

Author

Listed:
  • Li Ba
  • Mingshun Yang
  • Xinqin Gao
  • Yong Liu
  • Zhoupeng Han
  • Erbao Xu
  • Yan Li

Abstract

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.

Suggested Citation

  • Li Ba & Mingshun Yang & Xinqin Gao & Yong Liu & Zhoupeng Han & Erbao Xu & Yan Li, 2020. "A Mathematical Model and Self-Adaptive NSGA-II for a Multiobjective IPPS Problem Subject to Delivery Time," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, August.
    • Handle: RePEc:hin:jnlmpe:6012737
    DOI: 10.1155/2020/6012737
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