• Ateekh Ur Rehman 

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Globally small and medium-sized manufacturing enterprises (SMEs) are confronting serious challenges due to dynamic market demands. In pursuit of such situations they opt to be lean and flexible. Their demand have numerous varieties in product geometry and manufacturing processes. To cater such demand among the several existing strategies, reconfigurable manufacturing work cell (RWC) is the most prudent choice owing to its numerous benefits. This study offer an assessment lens to those who look for reconfiguration of their manufacturing setup. It is prudent to consider various RWC alternatives while wanting to reconfigure existing configuration change from time to time. The paper proposes a grey relational approach coupled with fuzzy analytical hierarchy weight to evaluate alternative RWCs. The simulation platform is adopted to exhibit performance of each RWC. The developed approach is to aid decision makers in identifying the most suitable RWC for the SME.

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References

  1. Y. Koren, W. Wang and X. Gu, "Value creation through design for scalability of reconfigurable manufacturing systems", Int. J. Prod. Res., vol. 55, no.5, pp. 1227–1242, 2017.
     Google Scholar
  2. E. Puik, D. Telgen, Van L. Moergestel and D. Ceglarek, "Assessment of reconfiguration schemes for Reconfigurable Manufacturing Systems based on resources and lead time", Robot. Comput.-Integr. Manuf. vol. 43, pp. 30–38, 2017.
     Google Scholar
  3. R. Dubey, A. Gunasekaran, P. Helo, T. Papadopoulos, S. J. Childe, and B. S. Sahay, "Explaining the impact of reconfigurable manufacturing systems on environmental performance: The role of top management and organizational culture", J. Clean. Prod., vol. 141, pp. 56–66, 2017.
     Google Scholar
  4. M. G. Mehrabi, A. G. Ulsoy and Y. Koren, "Reconfigurable manufacturing systems and their enabling technologies", Int. J. Manuf. Technol. Manag., vol. 1, no.1, pp. 114–131, 2000. doi:10.1504/IJMTM.2000.001330.
     Google Scholar
  5. A. L. Andersen, T. D. Brunoe, K. Nielsen and C. Rösiö, "Towards a generic design method for reconfigurable manufacturing systems: Analysis and synthesis of current design methods and evaluation of supportive tools", J. Manuf. Syst. vol. 42, pp. 179–195, 2017. doi:10.1016/j.jmsy.2016.11.006.
     Google Scholar
  6. Y. Koren, S. J. Hu and T. W. Weber, "Impact of Manufacturing System Configuration on Performance", CIRP Ann., vol 47, no. 1, pp. 369–372, 1998. doi:10.1016/S0007-8506(07)62853-4.
     Google Scholar
  7. A. Azab, H. ElMaraghy, P. Nyhuis, J. Pachow-Frauenhofer and M. Schmidt, "Mechanics of change: A framework to reconfigure manufacturing systems", CIRP J. Manuf. Sci. Technol., vol. 6, no. 2, pp.110–119, 2012. doi:10.1016/j.cirpj.2012.12.002.
     Google Scholar
  8. A. Bensmaine, M. Dahane and L. Benyoucef, "A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment", Comput. Ind. Eng., vol. 66, no. 3, pp.519–524, 2013. doi:10.1016/j.cie.2012.09.008.
     Google Scholar
  9. M. Maniraj and V. Pakkirisamy, "Justification of reconfigurable manufacturing systems selection using extended Brown-Gibson model and fuzzy TOPSIS", Int. J. Ind. Syst. Eng., vol. 20, no. 1, pp. 1–21, 2015. doi:10.1504/IJISE.2015.068995.
     Google Scholar
  10. L. N. Pattanaik and V. Kumar, "Multiple levels of reconfiguration for robust cells formed using modular machines", Int. J. Ind. Syst. Eng., vol. 5, no. 5, pp. 424–441, 2010. doi:10.1504/IJISE.2010.032965.
     Google Scholar
  11. F. Hasan, P. K. Jain and D. Kumar, "Performance modelling of dispatching strategies under resource failure scenario in reconfigurable manufacturing system", Int. J. Ind. Syst. Eng., vol. 16, no. 3, pp. 322–333, 2014. doi:10.1504/IJISE.2014.060132.
     Google Scholar
  12. H. G. Beyer and B. Sendhoff, "Robust optimization – A comprehensive survey", Comput. Methods Appl. Mech. Eng., vol. 196, no. 33-34, pp. 3190–3218, 2007. doi:10.1016/j.cma.2007.03.003.
     Google Scholar
  13. Y. M. Wang and K. S. Chin, “Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology”, International Journal of Approximate Reasoning, vol. 52, no. 4, pp. 541-553, 2011.
     Google Scholar