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An improved NSGAII-SA algorithm for the cell manufacturing system layout optimization problem

Author

Listed:
  • Honggen Chen

    (Zhengzhou University of Aeronautics
    Zhengzhou Key Laboratory of Industrial Digital Twins and Process Optimization)

  • Pengxiang Wang

    (Zhengzhou University of Aeronautics
    Zhengzhou Key Laboratory of Industrial Digital Twins and Process Optimization)

  • Jing Li

    (Zhengzhou University of Aeronautics
    Zhengzhou Key Laboratory of Industrial Digital Twins and Process Optimization)

  • Guohui Zhang

    (Zhengzhou University of Aeronautics
    Zhengzhou Key Laboratory of Industrial Digital Twins and Process Optimization)

  • Yan Zhang

    (Zhengzhou University of Aeronautics
    Zhengzhou University of Aeronautics)

Abstract

To address the challenges of significant logistics crossings and low production efficiency in traditional cluster layouts, a cellular manufacturing system (CMS) is commonly employed in diverse, small-batch production processes due to its high flexibility and adaptability. This study presents a comprehensive approach to effectively transform cluster layouts into cell manufacturing layouts, addressing the associated challenges. Initially, an improved fuzzy C-means clustering algorithm, enhanced with the elbow and the dissimilarity coefficient methods, is applied for cell division. Subsequently, a bi-objective optimization model is developed to minimize both the logistics distance and the layout area, with the NSGA-II-SA algorithm specifically tailored to handle the bi-objective sampling criterion. Thereafter, the layout optimization is performed, focusing on both the order and direction of the intracellular facilities. By applying the elbow method to the part-equipment matrix across various dimensions, its effectiveness in determining the optimal number of cell partitions is validated. Finally, the whole process of transforming the cluster layout into a CMS is successfully executed. The results demonstrate that the proposed algorithm outperforms non-dominated sorting genetic algorithm II (NSGA-II), the simulated annealing (SA) algorithm using random sampling (RM_SA), and the SA algorithm using bi-objective sampling (TM_SA) algorithms in both searchability and overall performance.

Suggested Citation

  • Honggen Chen & Pengxiang Wang & Jing Li & Guohui Zhang & Yan Zhang, 2025. "An improved NSGAII-SA algorithm for the cell manufacturing system layout optimization problem," Operational Research, Springer, vol. 25(1), pages 1-31, March.
    • Handle: RePEc:spr:operea:v:25:y:2025:i:1:d:10.1007_s12351-025-00899-0
    DOI: 10.1007/s12351-025-00899-0
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    References listed on IDEAS

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    1. Jerzy Grobelny & Rafal Michalski, 2017. "A novel version of simulated annealing based on linguistic patterns for solving facility layout problems," WORking papers in Management Science (WORMS) WORMS/17/07, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    2. Lee, Ching-Hung & Li, Li & Li, Fan & Chen, Chun-Hsien, 2022. "Requirement-driven evolution and strategy-enabled service design for new customized quick-response product order fulfillment process," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    3. Solimanpur, M. & Vrat, P. & Shankar, R., 2004. "Ant colony optimization algorithm to the inter-cell layout problem in cellular manufacturing," European Journal of Operational Research, Elsevier, vol. 157(3), pages 592-606, September.
    4. Xiaodong Zhang & Hongli Zhou & Dongfang Zhao, 2018. "Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, November.
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