Data envelopment analysis, operational research and uncertainty

This paper discusses a number of applications of data envelopment analysis and the nature of uncertainty in those applications. It then reviews the key approaches to handling uncertainty in data envelopment analysis (DEA) (imprecise DEA, bootstrapping, Monte Carlo simulation and chance constrained DEA) and considers their suitability for modelling the applications. The paper concludes with suggestions about the challenges facing an operational research analyst in applying DEA in real-world situations.

[1]  R. Dyson,et al.  Reducing Weight Flexibility in Data Envelopment Analysis , 1988 .

[2]  Cláudia S. Sarrico,et al.  Pitfalls and protocols in DEA , 2001, Eur. J. Oper. Res..

[3]  N. Petersen,et al.  Chance constrained efficiency evaluation , 1995 .

[4]  Theodor J. Stewart,et al.  Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis , 1996 .

[5]  R. Ackoff.  The Future of Operational Research is Past , 1979 .

[6]  William W. Cooper,et al.  Chapter 13 Satisficing DEA models under chance constraints , 1996, Ann. Oper. Res..

[7]  Howard Carter,et al.  Risk Analysis and Its Applications , 1984 .

[8]  Cláudia S. Sarrico,et al.  Data envelopment analysis and university selection , 1997 .

[9]  Ww Cooper.  Operational Research/Management Science: Where it's been. Where it should be going? , 1999 .

[10]  Robert G. Dyson,et al.  Exploring the use of DEA for formative evaluation in primary diabetes care: An application to compare English practices , 2009, J. Oper. Res. Soc..

[11]  C. Goodeve.  Operational Research , 1948, Nature.

[12]  P. W. Wilson,et al.  Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models , 1998 .

[13]  Harold O. Fried,et al.  The Measurement of Productive Efficiency and Productivity Growth , 2008 .

[14]  Joe Zhu,et al.  Imprecise data envelopment analysis (IDEA): A review and improvement with an application , 2003, Eur. J. Oper. Res..

[15]  O. Olesen.  Comparing and Combining Two Approaches for Chance Constrained DEA , 2006 .

[16]  Cláudia S. Sarrico,et al.  Using DEA for planning in UK universities—an institutional perspective , 2000, J. Oper. Res. Soc..

[17]  Kenneth C. Land,et al.  Chance‐constrained data envelopment analysis , 1993 .

[18]  William W. Cooper.  The Blackett Memorial Lecture 18 November 1997 , 1999, J. Oper. Res. Soc..

[19]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .

[20]  Chiang Kao,et al.  Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks , 2009, Eur. J. Oper. Res..

[21]  William W. CooperKyung.  IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA , 1999 .

[22]  Emmanuel Thanassoulis,et al.  Relative Efficiency Assessments Using Data Envelopment Analysis: An Application to Data on Rates Departments , 1987 .

[23]  Antreas D. Athanassopoulos,et al.  Assessing the Comparative Efficiency of Higher Education Institutions in the UK by the Means of Data Envelopment Analysis , 1997 .

[24]  P. W. Wilson,et al.  ASYMPTOTICS AND CONSISTENT BOOTSTRAPS FOR DEA ESTIMATORS IN NONPARAMETRIC FRONTIER MODELS , 2008, Econometric Theory.

[25]  A. U.S.,et al.  FORMULATION AND ESTIMATION OF STOCHASTIC FRONTIER PRODUCTION FUNCTION MODELS , 2001 .

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

[27]  R. Färe,et al.  A distance function approach to price efficiency , 1990 .

[28]  W. Meeusen,et al.  Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error , 1977 .

[29]  Rolf Färe,et al.  Applications of Modern Production Theory: Efficiency and Productivity , 1988 .

[30]  William W. Cooper,et al.  Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through , 1981 .

[31]  Mary Jo Bitner,et al.  Evaluating service encounters: The effects of physical surroundings and employee responses. , 1990 .

[32]  Anand Desai,et al.  Data envelopment analysis with stochastic variations in data , 2005 .