Directional congestion in the framework of data envelopment analysis

Abstract This study first proposes a definition for directional congestion in certain input and output directions within the framework of data envelopment analysis. Second, two methods from different viewpoints are proposed to estimate the directional congestion. Third, we address the relationship between directional congestion and classic (strong or weak) congestion. Finally, we present a case study investigating the analysis performed by the research institutes of the Chinese Academy of Sciences to demonstrate the applicability and usefulness of the methods developed in this study.

[1]  Mohsen Rostamy-Malkhalifeh,et al.  Measurement of Congestion in the Simultaneous Presence of Desirable and Undesirable Outputs , 2014, J. Appl. Math..

[2]  Waiman Cheung,et al.  Cone dominance and efficiency in DEA , 2006, Ann. Oper. Res..

[3]  Yong Zha,et al.  Distinguishing Technical Inefficiency from Desirable and Undesirable Congestion with an Application to Regional Industries in China , 2014 .

[4]  W. Cooper,et al.  Using DEA to improve the management of congestion in Chinese industries (1981-1997) , 2001 .

[5]  Kazuyuki Sekitani,et al.  DEA congestion and returns to scale under an occurrence of multiple optimal projections , 2009, Eur. J. Oper. Res..

[6]  M. Abbasi,et al.  Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis , 2014, TheScientificWorldJournal.

[7]  W. Cooper,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 1999 .

[8]  Gang Yu,et al.  Estimating returns to scale in DEA , 1997 .

[9]  R. Banker.  Estimating most productive scale size using data envelopment analysis , 1984 .

[10]  F. Hosseinzadeh Lotfi,et al.  A new method for measuring congestion in data envelopment analysis , 2010 .

[11]  Anthony T.. Flegg.  Congestion in the new British universities: A further analysis , 2009 .

[12]  R. Färe,et al.  Measuring congestion in production , 1983 .

[13]  Guoliang Yang,et al.  Negative data in DEA: Recognizing congestion and specifying the least and the most congested decision making units , 2017, Comput. Oper. Res..

[14]  William W. Cooper,et al.  Inefficiency and Congestion in Chinese Production Before and after the 1978 Economic Reforms , 1998 .

[15]  Joe Zhu,et al.  Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data , 2018, Eur. J. Oper. Res..

[16]  Victor V. Podinovski,et al.  Mixed partial elasticities in constant returns-to-scale production technologies , 2012, Eur. J. Oper. Res..

[17]  Hong Yan,et al.  Congestion and returns to scale in data envelopment analysis , 2004, Eur. J. Oper. Res..

[18]  William W. Cooper,et al.  Handbook on data envelopment analysis , 2011 .

[19]  Victor V. Podinovski,et al.  Differential Characteristics of Efficient Frontiers in Data Envelopment Analysis , 2010, Oper. Res..

[20]  Lei Fang,et al.  Congestion measurement in nonparametric analysis under the weakly disposable technology , 2015, Eur. J. Oper. Res..

[21]  Chiang Kao,et al.  Congestion measurement and elimination under the framework of data envelopment analysis , 2010 .

[22]  Kaoru Tone,et al.  Degree of scale economies and congestion: A unified DEA approach , 2002, Eur. J. Oper. Res..

[23]  Song Jin Yu,et al.  Multi-Stage data envelopment analysis congestion model , 2013, Oper. Res..

[24]  Rajiv D. Banker,et al.  Estimation of returns to scale using data envelopment analysis , 1992 .

[25]  R. Färe,et al.  The measurement of efficiency of production , 1985 .

[26]  William W. Cooper,et al.  Using DEA to Identify and Manage Congestion , 2004 .

[27]  Fang Xu,et al.  A study of DEA models without explicit inputs , 2011 .

[28]  Hurvey Leibenstein.  Allocative efficiency vs. X-Efficiency , 1966 .

[29]  G. Stigler.  The Xistence of X-Efficiency , 1976 .

[30]  William W. Cooper,et al.  Comparisons and evaluations of alternative approaches to the treatment of congestion in DEA , 2001, Eur. J. Oper. Res..

[31]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[32]  Rolf Färe,et al.  Measuring Productive Efficiency: An Application to Illinois Strip Mines , 1984 .

[33]  Gholam Reza Jahanshahloo,et al.  Suitable combination of inputs for improving outputs in DEA with determining input congestion: Considering textile industry of China , 2004, Appl. Math. Comput..

[34]  F. Hosseinzadeh Lotfi,et al.  Recognizing strong and weak congestion slack based in data envelopment analysis , 2013, Comput. Ind. Eng..

[35]  P. Zhou,et al.  Measuring Energy Congestion in Chinese Industrial Sectors: A Slacks-Based DEA Approach , 2015 .

[36]  Jie Wu,et al.  Congestion measurement for regional industries in China: A data envelopment analysis approach with undesirable outputs , 2013 .

[37]  Joseph C. Paradi,et al.  Marginal Rates and Two-dimensional Level Curves in DEA , 1998 .

[38]  Esmaile Khorram,et al.  Identification of congestion by means of integer-valued data envelopment analysis , 2016, Comput. Ind. Eng..

[39]  H. Leibenstein.  Individual Behavior and Economic Outcome. (Book Reviews: Beyond Economic Man. A New Foundation for Microeconomics) , 1976 .

[40]  Abraham Charnes,et al.  Programming with linear fractional functionals , 1962 .

[41]  Guo-liang Yang,et al.  Estimating directional returns to scale in DEA , 2017, INFOR Inf. Syst. Oper. Res..

[42]  R. Färe,et al.  Congestion of Production Factors , 1980 .