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Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL)

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

Abstract

The dynamic nature of today’s manufacturing industry, which is caused by the intense global competition and constant technological advancements, requires systems that are highly adaptive and responsive to demand fluctuations. Reconfigurable manufacturing systems (RMS) enable such responsiveness through their main characteristics. This paper addresses the problem of RMS configuration design, where the demand of a single product varies throughout its production life cycle, and the system configuration must change accordingly to satisfy the required demand with minimum cost. A two-phased method is developed to handle the primary system configuration design and the necessary system reconfigurations according to demand rate changes. This method takes advantage of Reconfigurable Machine Tools in RMS. In fact, by adding/removing modules to/from a specific modular reconfigurable machine, its production capability could be increased, with lower cost. A novel mixed integer linear programming formulation is presented in the second phase of the method to optimise the process of selecting the best possible transformation for the existing machine configurations. Two different cases are designed and solved by implementing the established method. The results of these cases in terms of capital cost, capacity expansion cost, unused capacity and number of transformations, are compared with two hypothetical scenarios. Analyses of the obtained results indicate the efficiency of the proposed approach and offer a promising outlook for further research.

Suggested Citation

  • Shokraneh K. Moghaddam & Mahmoud Houshmand & Omid Fatahi Valilai, 2018. "Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL)," International Journal of Production Research, Taylor & Francis Journals, vol. 56(11), pages 3932-3954, June.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:11:p:3932-3954
    DOI: 10.1080/00207543.2017.1412531
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    Cited by:

    1. Zhang, Yongjin & Zhao, Ming & Zhang, Yanjun & Pan, Ruilin & Cai, Jing, 2020. "Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers," European Journal of Operational Research, Elsevier, vol. 283(2), pages 491-510.
    2. 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.
    3. 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).
    4. 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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