DEA Cross-Efficiency Ranking Method Based on Grey Correlation Degree and Relative Entropy

The ranking of decision-making units (DMUs) is one of the most significant issues in efficiency evaluation. However, the calculation results from the traditional Data envelopment analysis(DEA), method sometimes include multiple efficient DMUs or multiple DMUs with the same efficiency value, in which case the approach is weak in distinguishing among these DMUs. Therefore, this study proposes a DEA cross-efficiency ranking method based on the relative entropy evaluation method and the grey relational analysis method. First, the approach uses the cross-efficiency matrix as the decision matrix of multiple criteria decision-making (MCDM), and the relationship between DMU and the ideal solution is analyzed by the grey relational analysis method and the relative entropy evaluation method. Then, the degree of the criteria is determined by Shannon entropy, and the weighted grey correlation degree and the weighted relative entropy are obtained. Finally, with the comprehensive relative closeness degree between the DMU and the ideal solution, we can sort all the DMUs accordingly. In a comparative analysis, it shows that this method analyzes the similarity between DMUs and the ideal solution from the information distance and the similarity of the data sequence curve, and has certain advantages for analyzing the ranking of DMUs.

[1]  T. Sexton,et al.  Data Envelopment Analysis: Critique and Extensions , 1986 .

[2]  T. Sueyoshi,et al.  A literature study for DEA applied to energy and environment , 2017 .

[3]  Ali Emrouznejad,et al.  A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016 , 2018 .

[4]  Carreño Carreño,et al.  Evaluación de la diversidad taxonómica y funcional de la comunidad microbiana relacionada con el ciclo del nitrógeno en suelos de cultivo de arroz con diferentes manejos del tamo , 2020 .

[5]  Jie Wu,et al.  DEA cross-efficiency evaluation based on Pareto improvement , 2016, Eur. J. Oper. Res..

[6]  F. Hosseinzadeh Lotfi,et al.  A cross-efficiency model based on super-efficiency for ranking units through the TOPSIS approach and its extension to the interval case , 2011, Math. Comput. Model..

[7]  Fanyong Meng,et al.  Interval cross efficiency for fully ranking decision making units using DEA/AHP approach , 2018, Ann. Oper. Res..

[8]  Chong Wu,et al.  Evaluating corporate social responsibility of airlines using entropy weight and grey relation analysis , 2015 .

[9]  Kwai-Sang Chin,et al.  A neutral DEA model for cross-efficiency evaluation and its extension , 2010, Expert Syst. Appl..

[10]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[11]  Ying Luo,et al.  Cross-efficiency evaluation based on ideal and anti-ideal decision making units , 2011, Expert Syst. Appl..

[12]  Wu Lijun,et al.  Shape optimization of welded plate heat exchangers based on grey correlation theory , 2017 .

[13]  Yves De Smet,et al.  Determining new possible weight values in PROMETHEE: a procedure based on data envelopment analysis , 2017, J. Oper. Res. Soc..

[14]  Dimitris K. Despotis,et al.  Improving the discriminating power of DEA: focus on globally efficient units , 2002, J. Oper. Res. Soc..

[15]  Liu Bei-shang,et al.  Relative entropy evaluation method for multiple attribute decision making , 2010 .

[16]  Fan Liu,et al.  An improvement in DEA cross-efficiency aggregation based on the Shannon entropy , 2018, Int. Trans. Oper. Res..

[17]  L. Liang,et al.  DEA cross-efficiency aggregation method based upon Shannon entropy , 2012 .

[18]  Mariagrazia Dotoli,et al.  A stochastic cross-efficiency data envelopment analysis approach for supplier selection under uncertainty , 2016, Int. Trans. Oper. Res..

[19]  Shu-Ping Wan,et al.  Virtual enterprise partner selection integrating LINMAP and TOPSIS , 2016, J. Oper. Res. Soc..

[20]  Jie Wu,et al.  Alternative secondary goals in DEA cross-efficiency evaluation , 2008 .

[22]  Qingyuan Zhu,et al.  Improving the evaluation of cross efficiencies: A method based on Shannon entropy weight , 2017, Comput. Ind. Eng..

[23]  Seyed Ali Rakhshan Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method , 2017, J. Oper. Res. Soc..

[24]  Saeed Zolfaghari,et al.  Review of efficiency ranking methods in data envelopment analysis , 2017 .

[25]  John S. Liu,et al.  Research fronts in data envelopment analysis , 2016 .

[26]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[27]  Toshiyuki Sueyoshi,et al.  A unified framework for the selection of a Flexible Manufacturing System , 1995 .

[28]  Edmundas Kazimieras Zavadskas,et al.  Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014 , 2015, Expert Syst. Appl..

[29]  Edmundas Kazimieras Zavadskas,et al.  A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015 , 2017 .

[30]  Rodney H. Green,et al.  Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses , 1994 .

[31]  Jie Wu,et al.  Ranking approach of cross-efficiency based on improved TOPSIS technique , 2011 .

[32]  Malin Song,et al.  A ranking method for DMUs with interval data based on dea cross-efficiency evaluation and TOPSIS , 2013 .

[33]  Yueh-Chiang Lee,et al.  Ranking DMUs by Combining Cross-Efficiency Scores Based on Shannon’s Entropy , 2019, Entropy.

[34]  Meng Meng,et al.  DEA and TOPSIS combination model for the construction scheduling of urban road projects , 2011, Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE).

[35]  Liguo Fei,et al.  A new method to identify influential nodes based on relative entropy , 2017 .

[36]  Jie Wu,et al.  A multiple criteria ranking method based on game cross-evaluation approach , 2012, Ann. Oper. Res..

[37]  Carlo Famoso,et al.  Passive and Active Vibrations Allow Self-Organization in Large-Scale Electromechanical Systems , 2016, Int. J. Bifurc. Chaos.

[38]  Zhongshan Yang,et al.  The measurement and influences of China's urban total factor energy efficiency under environmental pollution: Based on the game cross-efficiency DEA , 2019, Journal of Cleaner Production.

[39]  Andrés Camilo,et al.  Evaluación de la eficiencia relativa de los sistemas de producción porcícolas del departamento de Cundinamarca, utilizando análisis envolvente de datos (DEA) , 2020 .

[40]  F Hosseinzadeh Lotfi,et al.  A New Method for Ranking Efficient DMUs Based on TOPSIS and Virtual DMUs , 2012 .

[41]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .