A novel DEA model based on uncertainty theory
- 132 Downloads
Abstract
In deterministic DEA models, precise values are assigned to input and output data while they are intrinsically subjected to some degree of uncertainty. Most studies in this area are based on the assumption that inputs and outputs are equipped with some pre-known knowledge that enables one to use probability theory or fuzzy theory. In the lack of such data, one has to trust on the experts’ opinions, which can be considered as a sort of uncertainty. In this situation, the axiomatic approach of uncertainty theory initiated by Liu (Uncertainty theory. Berlin: Springer, 2007) could be an adequate powerful tool. Applying this theory, Wen et al. (J Appl Math, 2014; Soft Comput 1987–1996, 2015) suggested an uncertain DEA model while it has the disadvantage of pessimism. In this paper, we introduce another uncertain DEA model with the objective of acquiring the highest belief degree that the evaluated DMU is efficient. We also apply this model in ranking of the evaluated DMUs. Implementation of the model on different illustrative examples reveals that the ranks of DMUs are almost-stable in our model. This observation states that the rank of a DMU may roughly alternate with respect to the variation of minimum belief degrees. Our proposed model also compensates the rather optimistic point of view in the Wen et al. model that identifies all DMUs as efficient for higher belief degrees.
Keywords
DEA Uncertainty theory Uncertain distribution EfficiencyNotes
Acknowledgements
The authors would like to appreciate the anonymous referees, whom their comment are invaluable in enriching the manuscript. We also thank the Azarbaijan Shahid Madani University for its support.
References
- Allen, R., Athanassopoulos, A., Dyson, R. G., & Thanassoulis, E. (1997). Weights restrictions and value judgements in data envelopment analysis: Evolution, development and future directions. Annals of operations research, 73, 13–34.CrossRefGoogle Scholar
- Baykasoğlu, A., Gölcük, İ., & Akyol, D. E. (2017). A fuzzy multiple-attribute decision making model to evaluate new product pricing strategies, Annals of Operations Research, 251(1–2), 205–242.Google Scholar
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
- Cooper, W. W., Huang, Z., & Li, S. X. (1996). Satisficing DEA models under chance constraints. Annals of operations research, 66, 279–295.CrossRefGoogle Scholar
- Cooper, W. W., Park, K. S., & Yu, G. (1999). Idea and ar-idea: Models for dealing with imprecise data in DEA. Management Science, 45, 597–607.CrossRefGoogle Scholar
- Dyson, R. G., & Shale, E. A. (2010). Data envelopment analysis, operational research and uncertainty. Journal of the Operational Research Society, 61, 25–34.CrossRefGoogle Scholar
- Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9, 458–467.CrossRefGoogle Scholar
- Emrouznejad, A., & Tavana M., Eds. (2014). Performance measurement with fuzzy data envelopment analysis, Springer.Google Scholar
- Estellita Lins, M., Moreira da Silva, A. C., & Lovell, C. A. K. (2007). Avoiding infeasibility in DEA models with weight restrictions. European Journal of Operational Research, 181, 956–966.CrossRefGoogle Scholar
- Guo, P., & Tanaka, H. (2008). Decision making based on fuzzy data envelopment analysis. Intelligent Decision and Policy Making Support Systems, 117, 39–54.CrossRefGoogle Scholar
- Hatami-Marbini, A., Emrouznejad, A., & Tavana, M. (2011). A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making. European Journal of Operational Research, 214, 457–472.CrossRefGoogle Scholar
- Hatami-Marbini, A., Agrell, P. J., Fukuyama, H., Gholami, K., & Khoshnevis, P. (2017). The role of multiplier bounds in fuzzy data envelopment analysis. Annals of Operations Research, 250, 249–276.CrossRefGoogle Scholar
- Helmer, O. (1966). The Delphi method for systematizing judgments about the future. University of California: Institute of Government and Public Affairs.Google Scholar
- Huang, Z., Cheung, W., & Wang, H. (2006). Cone dominance and efficiency in DEA. Annals of Operations Research, 145, 89–103.CrossRefGoogle Scholar
- Inuiguchi, M., & Mizoshita, F. (2012). Qualitative and quantitative data envelopment analysis with interval data. Annals of Operations Research, 195, 189–220.CrossRefGoogle Scholar
- Kao, C., & Liu, S. T. (2000). Fuzzy efficiency measures in data envelopment analysis. Fuzzy Sets and Systems, 113, 427–437.CrossRefGoogle Scholar
- Lertworasirikul, S., Fang, S. C., A Joines, J., & LW Nuttle, H. (2003). Fuzzy data envelopment analysis (DEA): A possibility approach. Fuzzy Sets and Systems, 139, 379–394.CrossRefGoogle Scholar
- Lewis, H. F., & Sexton, T. R. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers & Operations Research, 31, 1365–1410.CrossRefGoogle Scholar
- Liu, B. (2007). Uncertainty theory. Berlin: Springer.CrossRefGoogle Scholar
- Liu, B. (2015). Uncertainty theory. Berlin: Springer.CrossRefGoogle Scholar
- Liu, B. (2009). Some research problems in uncertainty theory. Journal of Uncertain Systems, 3, 3–10.Google Scholar
- Liu, B. (2011). Uncertainty theory: A branch of mathematics for modeling human uncertainty. Berlin: Springer.Google Scholar
- Moreno, P., & Lozano, S. (2014). A network DEA assessment of team efficiency in the NBA. Annals of Operations Research, 214, 99–124.CrossRefGoogle Scholar
- Qin, R., Liu, Y. K., & Liu, Z. Q. (2011). Methods of critical value reduction for type-2 fuzzy variables and their applications. Journal of Computational and Applied Mathematics, 235, 1454–1481.CrossRefGoogle Scholar
- Sengupta, J. K. (1987). Data envelopment analysis for efficiency measurement in the stochastic case. Computers & Operations Research, 14, 117–129.CrossRefGoogle Scholar
- Sengupta, J. K. (1992). A fuzzy systems approach in data envelopment analysis. Computers & Mathematics with Applications, 24, 259–266.CrossRefGoogle Scholar
- Soleimani-Damaneh, M., Jahanshahloo, G. R., & Abbasbandy, S. (2006). Computational and theoretical pitfalls in some current performance measurement techniques; and a new approach. Applied Mathematics and Computation, 181, 1199–1207.CrossRefGoogle Scholar
- Wang, X., Gao, Z., & Guo, H. (2012). Delphi method for estimating uncertainty distributions. Information: An International Interdisciplinary Journal, 15, 449–460.Google Scholar
- Wang, Y. M., Greatbanks, R., & Yang, J. B. (2005). Interval efficiency assessment using data envelopment analysis. Fuzzy Sets and Systems, 153, 347–370.CrossRefGoogle Scholar
- Wen, M., Guo, L., Kang, R., & Yang, Y. (2014). Data envelopment analysis with uncertain inputs and outputs, Journal of Applied Mathematics, 2014, 307108.Google Scholar
- Wen, M., Qin, Z., Kang, R., Yang, Y. (2015). Sensitivity and stability analysis of the additive model in uncertain data envelopment analysis. Soft Computing, 19(7), 1987–1996.Google Scholar
- Xu, J., & Zhou, X. (2011). Fuzzy-like multiple objective decision making. Berlin: Springer.Google Scholar
- Zhou, X., Pedrycz, W., Kuang, Y., & Zhang, Z. (2016). Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation. Applied Soft Computing, 46, 424–440.CrossRefGoogle Scholar
- Zhou, X., Tu, Y., Hu, R., & Lev, B. (2015). A Class of Chance Constrained Linear Bi-Level Programming with Random Fuzzy Coefficients. In Proceedings of the 9th International Conference on Management Science and Engineering Management (pp. 423–433, Springer).Google Scholar
- Zhou, X., Luo, R., Tu, Y., Lev, B., & Pedrycz, W. (2017). Data envelopment analysis for bi-level systems with multiple followers. New Lebanon: Omega.Google Scholar