A Practical Common Weight Scalarizing Function Approach for Technology Selection
Subject Areas : Business StrategyMousa Amini 1 , Alireza Alinezhad 2
1 - Department of Industrial and Mechanical Engineering,
Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Department of Industrial and Mechanical Engineering,
Qazvin Branch, Islamic Azad University, Qazvin, Iran
Keywords: DEA, Weight Restriction, Common Set of Weights, Keywords: Technology Selection, Robot Selection, Scalarizing Function Approach, Discriminating Power,
Abstract :
Abstract. A practical common weight scalarizing function methodology with an improved discriminating power for technology selection is introduced. The proposed scalarizing function methodology enables the evaluation of the relative efficiency of decision-making units (DMUs) with respect to multiple outputs and a single exact input with common weights. Its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking obtained by the proposed scalarizing function framework with that obtained by the DEA classic model (CCR model) and Minimax method (Karsak & Ahiska,2005). Because the number of efficient DMUs is reduced so discriminating power of our approach is higher than previous approaches and because Spearman’s rank correlation between the ranks obtained from our approach and Minimax approach is high therefore robustness of our approach is justified.
References
[1] Agrawal, V.P., Kohli, V. and Gupta, S. (1991) ‘Computer aided robot selection: The multiple attribute decision-making approach’, Int. J. Prod. Res., Vol. 29, No. 8, pp. 1629–164.
[2] Agrawal, V.P., Verma, A. and Agarwal, S. (1992) ‘Computer-aided evaluation and selection of optimum grippers’, Int. J. Prod. Res., Vol. 30, No. 11, pp. 2713–2732.
[3] Alen, R., Athanasopoulos, A., Dyson, R.G. and Thanasoulis, E. (1997) ‘Weight restrictions And value judgements in data envelopment analysis: Evolution, development and future directions’, An. Oper. Res., Vol. 73, pp. 13–34.
[4] Baker, R.C. and Taluri, S. ‘A closer look at the use of data envelopment analysis for technology selection’, Comput. Ind. Eng., Vol. 32, No. 1, pp. 101–108.
[5] Booth, D.E., Khouja, M. and Hu, M. (1992) ‘A robust multivariate statistical procedure for evaluation and selection of industrial robots’, Int. J. Oper. Prod. Manag., Vol. 12, No. 2, pp. 15–24.
[6] Braglia, M. and Gabbrieli, R. (2000) ‘Dimensional analysis for investment selection in industrial robots’, Int. J. Prod. Res., Vol. 38, No. 18, pp. 4843–4848.
[7] Braglia, M. and Petroni, A. (1999) ‘Evaluating and selecting investments in industrial robots’, Int. J. Prod. Res., Vol. 37, No. 18, pp. 4157–4178.
[8] Chang, D.S. and Tsou, C.S. (1993) ‘A chance-constraints linear programming model on the economic evaluation of flexible manufacturing systems’, Prod. Plan. Contr., Vol. 4, pp. 159–165.
[9] Charnes, A., Cooper, W.W. and Rhodes, E. (1978) ‘Measuring the efficiency of decision-making units’, Eur. J. Oper. Res., Vol. 4, No. 2, pp. 429–44.
[10] Cook, W.D., Kres, M. and Seiford, L.M. (1996) ‘Data envelopment analysis in the presence of both quantitative and qualitative factors’, J. Oper. Res. Soc., Vol. 47, No. 7, pp. 945–953.
[11] Doyle, J. and Gren, R. (1994) ‘Efficiency and cros-efficiency in DEA: Derivations, meanings and uses’, J. Oper. Res. Soc., Vol. 45, No. 5, pp. 567–578.
[12] Dyson, R.G. and Thanasoulis, E. (1988) ‘Reducing weight flexibility in data envelopment analysis’, J. Oper. Res. Soc., Vol. 39, No. 6, pp. 563–576.
[13] Ghandforoush, P., Huang, P.Y. and Taylor, B.W. (1985) ‘A multi-criteria decision model for the selection of a computerized manufacturing control system’, Int. J. Prod. Res., Vol. 23, No. 1, pp. 17–128.
[14] Huang, P.Y. and Ghandforoush, P. (1984) ‘Procedures given for evaluating, selecting robots’, Ind. Eng., Vol. 16, pp. 4–48.
[15] Imany, M.M. and Schlesinger, R.J. (1989) ‘Decision models for robot selection: A comparison of Ordinary least squares and linear goal programming methods’, Dec.Sci., Vol. 20, No. 1, pp. 40–53.
[16] Karsak, E.E. (1998) ‘A two-phase robot selection procedure’, Prod. Plan. Contr., Vol. 9, No. 7, pp. 675–684.
[17] Karsak, E.E. (1999) ‘A DEA-based robot selection procedure incorporating fuzzy criteria values’, In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Tokyo, Japan, Vol. 1, pp. 1073–1078.
[18] Karsak, E.E. (2002) ‘Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives’, Int. J. Prod. Res., Vol. 40, No.13, pp. 3167–3181.
[19] Karsak, E.E. and Tolga, E. (2001) ‘Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments’, Int. J. Prod. Econ., Vol. 69, No. 1, pp. 49–64.
[20] Karsak, E.E. and ahiska, s.s. (2005) ‘Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection’, Int. J. Prod. Res., Vol. 43, No. 8, pp. 1537-1554.
[21] Khouja, M. (1995) ‘The use of data envelopment analysis for technology selection’, Comput. Ind. Eng., Vol. 28, No. 1, pp. 123–132.
[22] Khouja, M. and Ofodile, O.F. (1994) ‘The industrial robots selection problem: Literature review and directions for future research’, IE Trans., Vol. 26, No. 4, pp. 50–61.
[23] Khouja, M.J. and Kumar, R.L. (1999) ‘An options view of robot performance parameters in a dynamic environment’, Int. J. Prod. Res., Vol. 37, No. 6, pp. 1243–1257.
[24] Liang, G.S. and Wang, M.J.J. (1993) ‘A fuzzy multi-criteria decision-making approach for robot selection’, Robot. Comput. Integr. Manuf., Vol. 10, No. 4, pp. 267–274.
[25] McClave, J.T. and Benson, P.G. (1988) ‘Statistics for Business and Economics’, (Delen, San Francisco).
[26] Meredith, J.R. and Suresh, N.C. (1986) ‘Justifcation techniques for advanced manufacturing technologies’, Int. J. Prod. Res., Vol. 24, No. 5, pp. 1043–1057.
[27] Miltenburg, G.J. and Krinsky, I. (1987) ‘Evaluating flexible manufacturing systems’, IE Trans., Vol. 19, No. 2, pp. 22–233.
[28] Narasimhan,R.S. and Vickery,K. (1988) ‘An experimental evaluation of articulation of preferences in multiple criterion decision-making’, Dec. Sci., Vol. 19, No. 4, pp. 880–88.
[29] Parkan, C. and Wu, M.L. (1999) ‘Decision-making and performance measurement models with applications to robot selection’, Comput. Ind. Eng.,Vol. 26, No. 3, pp. 503–523.
[30] Parkan, C. and Wu, M.L. (2000) ‘Comparison of three modern multi-criteria decision-making tools’, Int. J. Sys. Sci., Vol. 31, No. 4, pp. 497–517.
[31] Perego, A. and Rangone, A. (1998) ‘A reference framework for the application of MADM fuzzy techniques to selecting AMTS’, Int. J. Prod. Res., Vol. 36, No. 2, pp. 437–458.
[32] Proctor, M.D. and Canada, J.R. (1992) ‘Past and present methods of manufacturing investment evaluation: A review of the empirical and theoretical literature’, Eng. Economist, Vol. 38, pp. 45–58.
[33] Raafat, F. (2002) ‘A comprehensive bibliography on justification of advanced manufacturing systems’, Int. J. Prod. Econ., Vol. 79, No. 3, pp. 197–208.
[34] Sambasivarao, K.V. and Deshmukh, S.G.(1997) ‘A decision support system for selection and justifcation of advanced manufacturing technologies’, Prod. Plan. Contr., Vol. 8, No. 3, pp. 270–284.
[35] Sarkis, J. and Taluri, S. (1999) ‘A decision model for evaluation of flexible manufacturing systems in the presence of both cardinal and ordinal factors’, Int. J. Prod. Res., Vol. 37, No. 13, pp. 2927–2938.
[36] Sexton, T.R., Silkman, R.H. and Hogan, A.J. (1986) ‘Data envelopment analysis: Critique and extensions. In Measuring Efficiency: An Assessment of Data Envelopment Analysis’, edited by R.H. Silkman, pp. 73–104, (Josey-Bas, San Francisco).
[37] Shang, J. and Sueyoshi, T. (1995) ‘A unified framework for the selection of a flexible manufacturing system’, Eur. J. Oper. Res., Vol. 85, No. 2, pp. 297–315.
[38] Son, Y.K. (1992) ‘A comprehensive bibliography on justification of advanced manufacturing technologies’, Eng. Economist, Vol. 38, No. 1, pp. 59–71.
[39] Stam, A. and Kula, M. (1991) ‘Selecting a flexible manufacturing system using multiple criteria analysis’, Int. J. Prod. Res., Vol. 29, No. 4, pp. 803–820.
[40] Sulivan, W.G. (1986) ‘Models IEs can use to include strategic, non-monetary factors in automation decisions’, Ind. Eng., Vol. 42, pp. 42–50.
[41] Sun, S. (2002) ‘Asesing computer numerical control machines using data envelopment analysis’, Int. J. Prod. Res., Vol. 40, No.9, pp. 2011–2039.
[42] Talluri, S.and Yoon, K.P. (2000) ‘A cone-ratio DEA approach for AMT justification’, Int. J. Prod. Econ., Vol. 66, No. 2, pp. 19–129.
[43] Tofalis, C. (1997) ‘Input efficiency profiling: an application to airlines’, Comput. Oper. Res., Vol. 24, pp. 253–258.
[44] Wierzbicki, A. (1980) ‘The use of reference objectives in multiobjective optimization’, In: Fandel G and T (eds). Multiple objective decision making, theory and application. Springer-Verlag, New York.
[45] Wierzbicki, A. (1986) ‘On the completeness and constructiveness of parametric characterizations to vector optimization problems’, OR Spektrum, Vol. 8, No. 2, pp. 73-87.