PL EN


2019 | 37 | 156-177
Article title

Multi-objective data envelopment analysis: A game of multiple attribute decision-making

Authors
Content
Title variants
Languages of publication
EN
Abstracts
EN
Aim/purpose ‒ The traditional data envelopment analysis (DEA) is popularly used to evaluate the relative efficiency among public or private firms by maximising each firm’s efficiency: the decision maker only considers one decision-making unit (DMU) at one time; thus, if there are n firms for computing efficiency scores, the resolution of n similar problems is necessary. Therefore, the multi-objective linear programming (MOLP) problem is used to simplify the complexity. Design/methodology/approach ‒ According to the similarity between the DEA and the multiple attribute decision making (MADM), a game of MADM is proposed to solve the DEA problem. Related definitions and proofs are provided to clarify this particular approach. Findings ‒ The multi-objective DEA is validated to be a unique MADM problem in this study: the MADM game for DEA is eventually identical to the weighting multi-objective DEA. This MADM game for DEA is used to rank ten LCD companies in Taiwan for their research and development (R&D) efficiencies to show its practical application. Research implications/limitations ‒ The main advantage of using an MADM game on the weighting multi-objective DEA is that the decision maker does not need to worry how to set these weights among DMUs/objectives, this MADM game will decide the weights among DMUs by the game theory. However, various DEA models are eventually evaluation tools. No one can guarantee us with 100% confidence that their evaluated results of DEA could be the absolute standard. Readers should analyse the results with care. Originality/value/contribution ‒ A unique link between the multi-objective CCR DEA and the MADM game for DEA is established and validated in this study. Previous scholars seldom explored and developed this breathtaking view before.
Year
Volume
37
Pages
156-177
Physical description
Contributors
author
  • Institute of Industrial Engineering and Management. College of Engineering. Da-Yeh University, Chang Hwa, Taiwan
References
  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. http://doi.org/10.1287/mnsc.30.9.1078
  • Barros, C. P., & Athanassiou, M. (2015). Efficiency in European seaports with DEA: Evidence from Greece and Portugal. In H. E. Haralambides (Eds), Port management (pp. 293-313). London: Palgrave Macmillan. http://doi.org/10.1057/9781137475770_14
  • Chan, S. H. (2003). Measuring innovative capability with patent indicators: The case of FPD industry in Taiwan (Master dissertation, National Chiao Tung University in Taiwan). Retrieved from https://www.lib.nctu.edu.tw
  • Charnes, A., Cooper, W. W., & Rhodes E. (1978). Measuring efficiency of decisionmaking unit. European Journal of Operational Research, 2(6), 429-444. http://doi.org/10.1016/0377-2217(78)90138-8
  • Chen, Y. W. (2004). An application of multi-objective game on multiple attribute group decision making problems: A case study in computer industry. Central European Journal of Operations Research, 12, 339-352.
  • Chen, Y. W. (2006). A group game of multiple attribute decision making. Asia-Pacific Journal of Operational Research, 24(5), 631-645. http://doi.org/10.1142/S0217595907001425
  • Chen, L., & Jia, G. (2017). Environmental efficiency analysis of China’s regional industry: A data envelopment analysis (DEA) based approach. Journal of Cleaner Production, 142(2), 846-853. http://doi.org/10.1016/j.jclepro.2016.01.045
  • Chen, Y. W., & Larbani, M. (2006). Two-person zero-sum game approach for fuzzy multiple attribute decision making problems. Fuzzy Sets and Systems, 157(1), 34-51. http://doi.org/10.1016/j.fss.2005.06.004
  • Chen, Y. W., Larbani, M., & Chang, Y.-P. (2009). Multi-objective data envelopment analysis. Journal of the Operational Research Society, 60(11), 1556-1566. http://doi.org/10.1057/jors.2009.92
  • Cohon, J. L. (1978). Multi-objective programming and planning. New York: Academic Press.
  • Golany, B. (1988). An interactive MOLP procedure for the extension of DEA to effectiveness analysis. Journal of Operational Research Society, 39, 725-734. http://doi.org/10.1057/palgrave.jors.0390803
  • Goodwin, P., & Wright, G. (2004). Decision analysis for management judgment. Chichester: Wiley.
  • Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making – methods and applications. New York: Springer. http://doi.org/10.1007/978-3-642-48318-9
  • Joro, T. (1998). Models for identifying target units in data envelopment analysis: Comparison and extension (Interim Report IR-98-055). Laxenburg: IIASA.
  • Joro, T., Korhonen, P., & Wallenius, J. (1998). Structural comparison of data envelopment analysis and multiple objective linear programming. Management Science, 44(7), 962-970. http://doi.org/10.1287/mnsc.44.7.962
  • Kao, C. (1994). Efficiency improvement in data envelopment analysis. European Journal of Operational Research, 78(3), 1-8. http://doi.org/10.1016/0377-2217(94)90243-7
  • Khalil, T. M. (2000). Management of technology: The key to competitiveness and wealth creation. New York: McGraw Hill.
  • Kornbluth, J. S. H. (1991). Analyzing policy effectiveness using cone restricted data envelopment analysis. Journal of Operational Research Society, 42(12), 1097-1104. http://doi.org/10.1038/sj/jors/0421206
  • Kacher, F., & Larbani, M. (2008). Existence of equilibrium solution for a non-cooperative game with fuzzy goals and parameters. Fuzzy Sets and Systems, 159(2), 164-176. http://doi.org/10.1016/j.fss.2007.05.018
  • Larbani, M. (2009). Non cooperative fuzzy games in normal form: A survey. Fuzzy Sets and Systems, 160(22), 3184-3210. http://doi.org/10.1016/j.fss.2009.02.026
  • Li, X.-B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. http://doi.org/10.1016/S0377-2217(98)00130-1
  • Miettinen, K. (1999). Nonlinear multiobjective optimization. Boston: Kluwer Academic Publishers.
  • Mousavi-Avval, S. H., Rafiee, S., Jafari, A., & Mohammadi, A. (2011). Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach. Applied Energy, 88(11), 3765-3772. http://doi.org/10.1016/j.apenergy.2011.04.021
  • Murphy, G. B., Trailer, J. W. & Hill, R. C. (1996). Measuring performance in entrepreneurship research. Journal of Business Research, 36(1), 15-23. http://doi.org/10.1016/0148-2963(95)00159-X
  • Naik, B., & Chakravarty, A. K. (1992). Strategic acquisition of new manufacturing technology: A review and research framework. International Journal of Production Research, 30(7), 1575-1601. http://doi.org/10.1080/00207549208948108
  • Osborne, M. J., & Rubinstein, A. (1994). A course in game theory. Cambridge, MA: Massachusetts Institute of Technology.
  • Steuer, R. E. (1986). Multiple criteria optimization: Theory, computation, and application. Florida: Krieger Publishing.
  • Stewart, T. J. (1996). Relationships between data envelopment analysis and multicriteria decision analysis. Journal of Operational Research Society, 47(5), 654-665. http://doi.org/10.1057/jors.1996.77
  • Yang, F., Wu, D., Liang, L., Bi, G., & Wu, D. D. (2011). Supply chain DEA: Production possibility set and performance evaluation model. Annals of Operations Research, 185(1), 195-211. http://doi.org/10.1007/s10479-008-0511-2
  • Yang, L., Ouyang, H., Fang, K., Ye, L., & Zhang, J. (2015). Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecological Indicators, 51, 13-19. http://doi.org/10.1016/j.ecolind.2014.08.040
  • Tseng, F. M., Chiu, Y. J., & Chen, J. S. (2009). Measuring business performance in the high-tech manufacturing industry: A case study of Taiwan’s large-sized TFT-LCDpanel companies. Omega, 37(3), 686-697. http://doi.org/10.1016/j.omega.2007.07.004
  • Zeleny, M. (1973). Compromise programming. In J. L. Cochrane & M. Zeleny (Eds.), Multiple Criteria Decision Making (pp. 262-301). Columbia: University of South Carolina Press.
Document Type
Publication order reference
Identifiers
ISSN
1732-1948
YADDA identifier
bwmeta1.element.cejsh-1b5197ac-dc7c-42a8-ab15-6e77d82142ca
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.