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A dynamic approach to measure machine and routing flexibilities of manufacturing systems

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  • Wahab, M.I.M.
  • Stoyan, S.J.

Abstract

The ability for a manufacturing system to easily adapt to various uncertainties in their production system is described as manufacturing flexibility. Over the past two decades manufacturing flexibility has become an important part of many production systems. Existing models, however, only include a few of the many technological attributes that can be found in manufacturing systems. This study considers a number of technological attributes that are common to manufacturing systems and incorporates them in the development of two manufacturing flexibility models that evaluate the performance of production systems. The first model defines a measure for machine flexibility and the second defines a measure for routing flexibility. Technological attributes such as the efficiency of processing an operation, the number of different operations a machine can perform, the fraction of an operation that can be transferred during disturbances, the probability of transferring an operation to alternative routes, are just a few of the attributes included in our flexibility models. We perform a set of tests that illustrate the strength of our models with respect to machine and routing flexibilities and highlight some of the weaknesses present in previous flexibility models.

Suggested Citation

  • Wahab, M.I.M. & Stoyan, S.J., 2008. "A dynamic approach to measure machine and routing flexibilities of manufacturing systems," International Journal of Production Economics, Elsevier, vol. 113(2), pages 895-913, June.
  • Handle: RePEc:eee:proeco:v:113:y:2008:i:2:p:895-913
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    References listed on IDEAS

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    1. Bernardo, John J. & Mohamed, Zubair, 1992. "The measurement and use of operational flexibility in the loading of Flexible Manufacturing Systems," European Journal of Operational Research, Elsevier, vol. 60(2), pages 144-155, July.
    2. Shuiabi, Eyas & Thomson, Vince & Bhuiyan, Nadia, 2005. "Entropy as a measure of operational flexibility," European Journal of Operational Research, Elsevier, vol. 165(3), pages 696-707, September.
    3. Beach, R. & Muhlemann, A. P. & Price, D. H. R. & Paterson, A. & Sharp, J. A., 2000. "A review of manufacturing flexibility," European Journal of Operational Research, Elsevier, vol. 122(1), pages 41-57, April.
    4. Wahab, M.I.M. & Wu, Desheng & Lee, Chi-Guhn, 2008. "A generic approach to measuring the machine flexibility of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 186(1), pages 137-149, April.
    5. Chandra, Pankaj & Tombak, Mihkel M., 1992. "Models for the evaluation of routing and machine flexibility," European Journal of Operational Research, Elsevier, vol. 60(2), pages 156-165, July.
    6. Chang, An-Yuan, 2007. "On the measurement of routing flexibility: A multiple attribute approach," International Journal of Production Economics, Elsevier, vol. 109(1-2), pages 122-136, September.
    7. Gupta, Yash P. & Goyal, Sameer, 1989. "Flexibility of manufacturing systems: Concepts and measurements," European Journal of Operational Research, Elsevier, vol. 43(2), pages 119-135, November.
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    Cited by:

    1. Seebacher, Gottfried & Winkler, Herwig, 2014. "Evaluating flexibility in discrete manufacturing based on performance and efficiency," International Journal of Production Economics, Elsevier, vol. 153(C), pages 340-351.
    2. Enrico Teich & Thorsten Claus, 2017. "Measurement of Load and Capacity Flexibility in Manufacturing," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(4), pages 291-302, December.
    3. Tomino, Takahiro & Park, Youngwon & Hong, Paul & Roh, James Jungbae, 2009. "Market flexible customizing system (MFCS) of Japanese vehicle manufacturers: An analysis of Toyota, Nissan and Mitsubishi," International Journal of Production Economics, Elsevier, vol. 118(2), pages 375-386, April.
    4. Li, Xinyu & Shao, Xinyu & Gao, Liang & Qian, Weirong, 2010. "An effective hybrid algorithm for integrated process planning and scheduling," International Journal of Production Economics, Elsevier, vol. 126(2), pages 289-298, August.
    5. Choe, Pilsung & Tew, Jeffrey D. & Tong, Songzhen, 2015. "Effect of cognitive automation in a material handling system on manufacturing flexibility," International Journal of Production Economics, Elsevier, vol. 170(PC), pages 891-899.
    6. Baykasoglu, Adil & Ă–zbakIr, Lale, 2010. "Analyzing the effect of dispatching rules on the scheduling performance through grammar based flexible scheduling system," International Journal of Production Economics, Elsevier, vol. 124(2), pages 369-381, April.

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