An Entropy Based Shapley Value for Ranking in Data Envelopment Analysis
الموضوعات :Reza Fallahnejad 1 , Sanaz Asadirahmati 2 , Kaivan Moradipour 3
1 - Department of Mathematics, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran
2 - Department of Mathematics, Technical and Vocational University (TVU), Tehran, Iran.
3 - Department of Mathematics, Technical and Vocational University (TVU), Tehran, Iran.
الکلمات المفتاحية: Data envelopment analysis, Ranking, Entropy, Cooperative Game, Shapley value,
ملخص المقالة :
In traditional DEA, DMUs are divided into Efficient and inefficient, but the score of all efficient units are equal to one and there is no discrimination between them. Thus many ranking methods are proposed to increase discrimination power. This paper proposes an integrated framework of cooperative games and entropy to rank efficient units by considering efficient units as players in a cooperative game, A subset of these players is defined as the coalition of S. The sum of the efficiency of inefficient DMUs with respect to the frontier of production possibility set contain inefficient DMUs and the member of coalition S is defined as the characteristic function of the coalition S, which is used to determine the marginal effect of efficient DMUs. Then, a new Shapley Value resulted from aggregating the marginal effects of efficient DMUs weighted by Shannon entropy is used for ranking efficient DMUs. For the first time, we use the entropy to create a Shapley value for calculating the rank of efficient units.. Two examples are provided to illustrate the applicability of proposed model.
Aldamak, A., & Zolfaghari, S. (2017). Review of efficiency ranking methods in data envelopment analysis. Measurement, 106, 161-172.
An, Q., Wang, P., & Shi, S. (2020). Fixed cost allocation for two-stage systems with cooperative relationship using data envelopment analysis. Computers & Industrial Engineering, 145, 106534.
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.
Bian, Y., & Yang, F. (2010). Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon’s entropy. Energy Policy, 38(4), 1909-1917.
Çakır, S. (2017). Proposing integrated Shannon’s entropy–inverse data envelopment analysis methods for resource allocation problem under a fuzzy environment. Engineering Optimization, 49(10), 1733-1749.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Driessen, T. S. (2013). Cooperative games, solutions and applications (Vol. 3). Springer Science & Business Media.
Du, J., Liang, L., Chen, Y., Cook, W. D., & Zhu, J. (2011). A bargaining game model for measuring performance of two-stage network structures. European Journal of Operational Research, 210(2), 390-397.
Ghosh, S., Yadav, V. K., Mukherjee, V., & Yadav, P. (2017). Evaluation of relative impact of aerosols on photovoltaic cells through combined Shannon's entropy and Data Envelopment Analysis (DEA). Renewable energy, 105, 344-353.
Hinojosa, M. A., Lozano, S., Borrero, D. V., & Mármol, A. M. (2017). Ranking efficient DMUs using cooperative game theory. Expert Systems with Applications, 80, 273-283.
Hsiao, B., Chern, C. C., & Chiu, C. R. (2011). Performance evaluation with the entropy-based weighted Russell measure in data envelopment analysis. Expert Systems with Applications, 38(8), 9965-9972.
Huang, C., Mi, X., & Kang, B. (2021). Basic probability assignment to probability distribution function based on the Shapley value approach. International Journal of Intelligent Systems, 36(8), 4210-4236.
Jie, W. U., Liang, L., & ZHA, Y. C. (2008). Determination of the weights of ultimate cross efficiency based on the solution of nucleolus in cooperative game. Systems Engineering-Theory & Practice, 28(5), 92-97.
Lee, Y. C. (2019). Ranking DMUs by combining cross-efficiency scores based on Shannon’s entropy. Entropy, 21(5), 467.
Li, Y., & Liang, L. (2010). A Shapley value index on the importance of variables in DEA models. Expert systems with Applications, 37(9), 6287-6292.
Lo Storto, C. (2016). Ecological efficiency based ranking of cities: A combined DEA cross-efficiency and Shannon’s entropy method. Sustainability, 8(2), 124.
Mahmoudi, R., Emrouznejad, A., & Rasti-Barzoki, M. (2019). A bargaining game model for performance assessment in network DEA considering sub-networks: a real case study in banking. Neural Computing and Applications, 31(10), 6429-6447.
Nakabayashi, K., & Tone, K. (2006). Egoist's dilemma: a DEA game. Omega, 34(2), 135-148.
Qi, X. G., & Guo, B. (2014). Determining common weights in data envelopment analysis with Shannon’s entropy. Entropy, 16(12), 6394-6414.
Rotela Junior, P., Rocha, L. C. S., Aquila, G., Balestrassi, P. P., Peruchi, R. S., & Lacerda, L. S. (2017). Entropic data envelopment analysis: a diversification approach for portfolio optimization. Entropy, 19(9), 352.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.
Si, Q., & Ma, Z. (2019). DEA cross-efficiency ranking method based on grey correlation degree and relative entropy. Entropy, 21(10), 966.
Soleimani-Damaneh, M., & Zarepisheh, M. (2009). Shannon’s entropy for combining the efficiency results of different DEA models: Method and application. Expert Systems with Applications, 36(3), 5146-5150.
Su, C. H., & Lu, T. (2019). An entropy-based cross-efficiency under variable returns to scale. Entropy, 21(12), 1205.
Wang, L., Li, L., & Hong, N. (2016). Entropy cross-efficiency model for decision making units with interval data. Entropy, 18(10), 358.
Wu, H., Du, S., Liang, L., & Zhou, Y. (2013). A DEA-based approach for fair reduction and reallocation of emission permits. Mathematical and computer modelling, 58(5-6), 1095-1101.
Wu, J., Liang, L., & Yang, F. (2009). Determination of the weights for the ultimate cross efficiency using Shapley value in cooperative game. Expert Systems with Applications, 36(1), 872-876.
Wu, J., Liang, L., Yang, F., & Yan, H. (2009). Bargaining game model in the evaluation of decision making units. Expert Systems with Applications, 36(3), 4357-4362.
Wu, J., Sun, J., Liang, L., & Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162-5165.
Wu, J., Zhu, Q., Cook, W. D., & Zhu, J. (2016). Best cooperative partner selection and input resource reallocation using DEA. Journal of the Operational Research Society, 67(9), 1221-1237.
Xie, Q., Dai, Q., Li, Y., & Jiang, A. (2014). Increasing the discriminatory power of DEA using Shannon’s entropy. Entropy, 16(3), 1571-1585.
Yang, Z., Wang, X., & Sun, D. (2010). Using the bootstrap method to detect influential DMUs in data envelopment analysis. Annals of Operations Research, 173(1), 89-103.
Zhou, Z., Sun, L., Yang, W., Liu, W., & Ma, C. (2013). A bargaining game model for efficiency decomposition in the centralized model of two-stage systems. Computers & Industrial Engineering, 64(1), 103-108.