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A multi-population co-evolutionary genetic programming approach for optimal mass customisation production

Author

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  • Biao Yu
  • Han Zhao
  • Deyi Xue

Abstract

Development of mass customised products demands various activities in the product development process, such as design, manufacturing process planning, manufacturing resource planning and maintenance process planning, to be considered and coordinated. In this research, a multi-population co-evolutionary genetic programming (MCGP) approach is introduced to identify the optimal design and its downstream product life cycle activities for developing mass customised product considering these different product life cycle activities and their relationships. In this research, two types of relationships between downstream product life cycle activities are considered: sequential relationships and concurrent relationships. The product design and its downstream life cycle descriptions are modelled by a multi-level graph data structure. These product life cycle descriptions are defined at two different levels: generic level for modelling the descriptions in a product family and specific level for modelling the descriptions of a customised product. The optimal design and its downstream life cycle activities are identified through the MCGP approach based on evaluations in different product life cycle aspects. Various methods have been developed to improve computation efficiency for the MCGP. Industrial case studies and comparative case studies have been implemented to demonstrate the effectiveness of the developed approach.

Suggested Citation

  • Biao Yu & Han Zhao & Deyi Xue, 2017. "A multi-population co-evolutionary genetic programming approach for optimal mass customisation production," International Journal of Production Research, Taylor & Francis Journals, vol. 55(3), pages 621-641, February.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:3:p:621-641
    DOI: 10.1080/00207543.2016.1194538
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    Cited by:

    1. Xia, Tangbin & Dong, Yifan & Xiao, Lei & Du, Shichang & Pan, Ershun & Xi, Lifeng, 2018. "Recent advances in prognostics and health management for advanced manufacturing paradigms," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 255-268.
    2. Andrzej Szajna & Mariusz Kostrzewski, 2022. "AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times," Sustainability, MDPI, vol. 14(11), pages 1-17, May.

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