Entropy Cross-Efficiency Model for Decision Making Units with Interval Data

The cross-efficiency method, as a Data Envelopment Analysis (DEA) extension, calculates the cross efficiency of each decision making unit (DMU) using the weights of all decision making units (DMUs). The major advantage of the cross-efficiency method is that it can provide a complete ranking for all DMUs. In addition, the cross-efficiency method could eliminate unrealistic weight results. However, the existing cross-efficiency methods only evaluate the relative efficiencies of a set of DMUs with exact values of inputs and outputs. If the input or output data of DMUs are imprecise, such as the interval data, the existing methods fail to assess the efficiencies of these DMUs. To address this issue, we propose the introduction of Shannon entropy into the cross-efficiency method. In the proposed model, intervals of all cross-efficiency values are firstly obtained by the interval cross-efficiency method. Then, a distance entropy model is proposed to obtain the weights of interval efficiency. Finally, all alternatives are ranked by their relative Euclidean distance from the positive solution.

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

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

[3]  Joe Zhu,et al.  DEA Cross Efficiency , 2014 .

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

[5]  Shinn Sun,et al.  Assessing computer numerical control machines using data envelopment analysis , 2002 .

[6]  Joe Zhu,et al.  DEA Cobb–Douglas frontier and cross-efficiency , 2014, J. Oper. Res. Soc..

[7]  Guo-liang Yang,et al.  Cross-efficiency aggregation in DEA models using the evidential-reasoning approach , 2013, Eur. J. Oper. Res..

[8]  Sungmook Lim,et al.  Minimax and maximin formulations of cross-efficiency in DEA , 2012, Comput. Ind. Eng..

[9]  G. Jahanshahloo Data Envelopment Analysis with Imprecise Data , 2011 .

[10]  K. Chin,et al.  Some alternative models for DEA cross-efficiency evaluation , 2010 .

[11]  Rong-Tsu-S.-U. Wang,et al.  Measuring production and marketing efficiency using grey relation analysis and data envelopment analysis , 2010 .

[12]  Liang Liang,et al.  DEA game cross-efficiency approach to Olympic rankings , 2009 .

[13]  Ying-Ming Wang,et al.  A general multiple attribute decision-making approach for integrating subjective preferences and objective information , 2006, Fuzzy Sets Syst..

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

[15]  Kwai-Sang Chin,et al.  Fuzzy data envelopment analysis: A fuzzy expected value approach , 2011, Expert Syst. Appl..

[16]  William W. Cooper,et al.  Data Envelopment Analysis: History, Models, and Interpretations , 2011 .

[17]  Qiang Cui,et al.  Evaluating energy efficiency for airlines: An application of VFB-DEA , 2015 .

[18]  Zilla Sinuany-Stern,et al.  Combining ranking scales and selecting variables in the DEA context: the case of industrial branches , 1998, Comput. Oper. Res..

[19]  Peng Jiang,et al.  Weight determination in the cross-efficiency evaluation , 2011, Comput. Ind. Eng..

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

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

[22]  Corrado lo Storto Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method , 2016 .

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

[24]  Leigh Drake,et al.  The use of data envelopment analysis to monitor hotel productivity , 1997 .

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

[26]  Soung Hie Kim,et al.  Identification of inefficiencies in an additive model based IDEA (imprecise data envelopment analysis) , 2002, Comput. Oper. Res..

[27]  Michael Z. Hanani,et al.  Combining the AHP and DEA methodologies for selecting the best alternative , 2011 .

[28]  Ying Luo,et al.  Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making , 2010, Math. Comput. Model..

[29]  Gholam R. Amin,et al.  Maximum appreciative cross-efficiency in DEA: A new ranking method , 2015, Comput. Ind. Eng..

[30]  Jie Wu,et al.  Improved interval DEA models with common weight , 2014, Kybernetika.

[31]  Jie Wu,et al.  Determination of the weights for the ultimate cross efficiency using Shapley value in cooperative game , 2009, Expert Syst. Appl..

[32]  Adli Mustafa,et al.  Cross-ranking of Decision Making Units in Data Envelopment Analysis , 2013 .

[33]  Jian-Bo Yang,et al.  Interval efficiency assessment using data envelopment analysis , 2005, Fuzzy Sets Syst..

[34]  Ying-Ming Wang,et al.  Approaches to determining the relative importance weights for cross-efficiency aggregation in data envelopment analysis , 2013, J. Oper. Res. Soc..

[35]  T. Anderson,et al.  The Fixed Weighting Nature of A Cross-Evaluation Model , 2002 .

[36]  F. Hosseinzadeh Lotfi,et al.  Selecting symmetric weights as a secondary goal in DEA cross-efficiency evaluation , 2011 .

[37]  Gerhard Reichmann,et al.  University library benchmarking: An international comparison using DEA , 2006 .

[38]  Farhad Hosseinzadeh Lotfi,et al.  Optimising proportional weights as a secondary goal in DEA cross-efficiency evaluation , 2014 .

[39]  I. Contreras,et al.  Optimizing the rank position of the DMU as secondary goal in DEA cross-evaluation , 2012 .

[40]  Bo Guo,et al.  Determining Common Weights in Data Envelopment Analysis with Shannon's Entropy , 2014, Entropy.

[41]  Joe Zhu,et al.  Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market , 2014, Eur. J. Oper. Res..

[42]  J. Cubbin,et al.  Public Sector Efficiency Measurement: Applications of Data Envelopment Analysis , 1992 .

[43]  Joe Zhu,et al.  DEA Cross Efficiency Under Variable Returns to Scale , 2015 .

[44]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[45]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[46]  Gang Yu,et al.  An Illustrative Application of Idea (Imprecise Data Envelopment Analysis) to a Korean Mobile Telecommunication Company , 2001, Oper. Res..

[47]  Nuria Ramón,et al.  Reducing differences between profiles of weights: A "peer-restricted" cross-efficiency evaluation , 2011 .

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

[49]  Yongjun Li,et al.  Increasing the Discriminatory Power of DEA Using Shannon's Entropy , 2014, Entropy.

[50]  Marcello Braglia,et al.  Evaluating and selecting investments in industrial robots , 1999 .

[51]  Zilla Sinuany-Stern,et al.  Review of ranking methods in the data envelopment analysis context , 2002, Eur. J. Oper. Res..

[52]  Wade D. Cook,et al.  Performance measurement and classification data in DEA: Input-oriented model , 2007 .

[53]  Mariagrazia Dotoli,et al.  A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of Decision Making Units under uncertainty , 2015, Comput. Ind. Eng..

[54]  Zilla Sinuany-Stern,et al.  An AHP/DEA methodology for ranking decision making units , 2000 .

[55]  R. Jahed,et al.  AN IMPROVEMENT FOR EFFICIENCY INTERVAL: EFFICIENT AND INEFFICIENT FRONTIERS , 2011 .

[56]  Fabio Sciancalepore,et al.  Using a DEA-cross efficiency approach in public procurement tenders , 2012, Eur. J. Oper. Res..

[57]  H. C. Co,et al.  Performance and R&D expenditures in American and Japanese manufacturing firms , 1997 .