Flexible manufacturing system selection using preference ranking methods : A comparative study

Abstract: Flexible manufacturing systems (FMSs) offer opportunities for the manufacturers to improve their technology, competitiveness and profitability through a highly efficient and focused approach to manufacturing effectiveness. Justification, evaluation and selection of FMSs have now been receiving significant attention in the manufacturing environment. Evaluating alternative FMSs in the presence of multiple conflicting criteria and performance measures is often a difficult task for the decision maker. Preference ranking tools are special types of multi-criteria decision-making methods in which the decision maker’s preferences on criteria are aggregated together to arrive at the final evaluation and selection of the alternatives. This paper deals with the application of six most potential preference ranking methods for selecting the best FMS for a given manufacturing organization. It is observed that although the performances of these six methods are almost similar, ORESTE (Organization, Rangement Et Synthese De Donnes Relationnelles) method slightly outperforms the others. These methods use some preference function or utility value or Besson ranking of criteria and alternatives, to indicate how much an alternative is preferred to the others. Most of these methods need quantification of criteria weights or different preference parameters, but ORESTE method, being an ordinal outranking approach, only requires ordinal data and attribute rankings according to their importance. Therefore, it is particularly applicable to those situations where the decision maker is unable to provide crisp evaluation data and attribute weights.

How to cite this paper

 Chatterjee, P & Chakraborty, S. (2014). Flexible manufacturing system selection using preference ranking methods : A comparative study.International Journal of Industrial Engineering Computations , 5(2), 315-338.

Refrences

Bayazit, O. (2005). Use of AHP in decision-making for flexible manufacturing systems. Journal of Manufacturing Technology and Management, 16(7), 808-819.

Bhattacharya, A., Abraham, A., Vasant, P., & Grosan, C. (2007). Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker. International Journal of Innovative Computing, Information and Control, 3(1), 131-140.

Borenstein, D. (1998). A visual interactive multicriteria decision analysis model for FMS design. International Journal of Advanced Manufacturing Technology, 14(11), 848-857.

Chan, F.T.S., Jiang, B., & Tang, N.K.H. (2000). The development of intelligent decision support tools to aid the design of flexible manufacturing systems. International Journal of Production Economics, 65(1), 73-84.

Doumpos, M., & Zopounidis, C. (2004). A multi-criteria classification approach based on pair-wise comparison. European Journal of Operational Research, 158(2), 378-389.

Hajkowicz, S., & Higgins, A. (2008). A comparison of multiple criteria analysis techniques for water resource management. European Journal of Operational Research, 184(1), 255-265.

Kaklauskas, A., Zavadskas, E.K., Raslanas, S., Ginevicius, R., Komka, A., & Malinauskas, P. (2006). Selection of low e-windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case. Energy and Buildings, 38(5), 454-462.

Kaklauskas, A., Zavadskas, E.K., & Trinkunas, V. (2007). A multiple criteria decision support on-line system for construction. Engineering Applications of Artificial Intelligence, 20(2), 163-175.

Karsak, E.E., & Tolga, E. (2001). Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments. International Journal of Production Economics, 69(1), 49-64.

Karsak, E.E., & Kuzgunkaya, O. (2002). A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system. International Journal of Production Economics, 79(2), 101-111.

Karsak, E.E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. International Journal of Production Research, 40(13), 3167-3181.

Karsak, E.E. (2008). Using data envelopment analysis for evaluating flexible manufacturing systems in the presence of imprecise data. International Journal of Advanced Manufacturing Technology, 35(9-10), 867-874.

Kulak, O., & Kahraman, C. (2005). Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach. International Journal of Production Economics, 95(3), 415-424.

Liu, S-T. (2008). A fuzzy DEA/AR approach to the selection of flexible manufacturing systems. Computers & Industrial Engineering, 54(1), 66-76.

Lotfi, V. (1995). Implementing flexible automation: A multiple criteria decision making approach. International Journal of Production Economics, 38(2-3), 255-268.

Martel, J.M., & Matarazzo, B. (2005). Other outranking approaches. In: Figueira J, Salvatore G, Ehrgott M. (Eds.) Multiple Criteria Decision Analysis: State of the Art Surveys. Springer: New York.

Mehrabad, M.S., & Anvari, M. (2010). Provident decision making by considering dynamic and fuzzy environment for FMS evaluation. International Journal of Production Research, 48(15), 4555-4584.

O’Grady, P.J., & Menon, U. (1986). A concise review of flexible manufacturing systems and FMS literature. Computers in Industry, 7(2), 155-167.

Ovchinnikov, S., & Roubens, M. (1992). On fuzzy strict preference, indifference, and incomparability relations. Fuzzy Sets and Systems, 49(1), 15-20.

Pastijn, H., & Leysen, J. (1989). Constructing an outranking relation with ORESTE. Mathematical and Computer Modelling, 12(10-11), 1255-1268.

Parkan, C., & Wu, M-L. (1997). On the equivalence of operational performance measurement and multiple attribute decision making. International Journal of Production Research, 35(11), 2963-2988.

Parkan, C., & Wu, M-L. (2000). Comparison of three modern multicriteria decision-making tools. International Journal of Systems Science, 31(4), 497-517.

Raju, K.S., & Kumar, D.N. (1999). Multicriterion decision making in irrigation planning. Agricultural Systems, 62(2), 117-129.

Rao, R.V. (2006). A decision-making framework model for evaluating flexible manufacturing systems using digraph and matrix methods. International Journal of Advanced Manufacturing Technology, 30(11-12), 1101-1110.

Rao, R.V. (2008). Evaluating flexible manufacturing systems using a combined multiple attribute decision making method. International Journal of Production Research, 46(7), 1975-1989.

Rao, R.V., & Parnichkun, M. (2009). Flexible manufacturing system selection using a combinatorial mathematics-based decision-making method. International Journal of Production Research, 47(24), 6981-6998.

Rao, R.V. (2009). Flexible manufacturing system selection using an improved compromise ranking method. International Journal of Industrial and System Engineering, 4(2), 198-215.

Rao, R.V., & Singh, D. (2011). Evaluating flexible manufacturing systems using Euclidean distance-based integrated approach. International Journal of Decision Sciences, Risk and Management, 3(1-2), 32-53.

Roubens, M. (1982). Preference relations on actions and criteria in multicriteria decision making. European Journal of Operational Research, 10(1), 51-55.

Sarkis, J. (1997). Evaluating flexible manufacturing systems alternatives using data envelopment analysis. The Engineering Economist, 43(1), 25-47.

Shang, J., & Sueyoshi, T. (1995). A unified framework for the selection of a flexible manufacturing system. European Journal of Operational Research, 85(2), 297-315.

Talluri, S., Whiteside, M.M., & Seipel, S.J. (2000). A nonparametric stochastic procedure for FMS evaluation. European Journal of Operational Research, 124(3), 529-538.

Teghem, J., Delhaye, C., & Kunsch, P.L. (1989). An interactive decision support system (IDSS) for multicriteria decision aid. Mathematical and Computer Modelling, 12(10-11), 1311-1320

Turskis, Z., & Zavadskas, E.K. (2010). A novel method for multiple criteria analysis: Grey additive ratio assessment (ARAS-G) method. Informatica, 21(4), 597-610.

Wabalackis, R.N. (1988). Justification of FMS with the analytic hierarchy process. Journal of Manufacturing Systems, 7(3), 175-182.

Zavadskas, E.K., Kaklauskas, A., Turskis, Z., & Tamo?aitien?, J. (2008). Selection of the effective dwelling house walls by applying attributes values determined at intervals. Journal of Civil Engineering & Management, 14(2), 85-93.

Zavadskas, E.K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy, 16(2), 159-172.