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Configuration design of scalable reconfigurable manufacturing systems for part family

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  • Shokraneh K. Moghaddam
  • Mahmoud Houshmand
  • Kazuhiro Saitou
  • Omid Fatahi Valilai

Abstract

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.

Suggested Citation

  • Shokraneh K. Moghaddam & Mahmoud Houshmand & Kazuhiro Saitou & Omid Fatahi Valilai, 2020. "Configuration design of scalable reconfigurable manufacturing systems for part family," International Journal of Production Research, Taylor & Francis Journals, vol. 58(10), pages 2974-2996, May.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:10:p:2974-2996
    DOI: 10.1080/00207543.2019.1620365
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    Cited by:

    1. Carlos Alberto Barrera-Diaz & Amir Nourmohammadi & Henrik Smedberg & Tehseen Aslam & Amos H. C. Ng, 2023. "An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems," Mathematics, MDPI, vol. 11(6), pages 1-23, March.
    2. Delorme, Xavier & Cerqueus, Audrey & Gianessi, Paolo & Lamy, Damien, 2023. "RMS balancing and planning under uncertain demand and energy cost considerations," International Journal of Production Economics, Elsevier, vol. 261(C).
    3. Gaurav Kumar & Kapil Kumar Goyal & Neel Kamal Batra & Deepika Rani, 2022. "Single part reconfigurable flow line design using fuzzy best worst method," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 603-631, June.

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