Non-Discretionary Factors and Imprecise Data in Dea
Int. Journal of Math. Analysis, Vol. 1, 2007, no. 5, 237 - 246
Non-Discretionary Factors and Imprecise Data in DEA
F. Hosseinzadeh Lotfi,1 and G. R. Jahanshahloo Dept. of Math., Science and Research Branch Islamic Azad University, Tehran 14515-775, Iran M. Esmaeili Dept. of Math., Islamic Azad University, Shahrekord P.O. Box 166, Shahrekord, Iran
Abstract Discretionary models of data envelopment analysis (DEA) assume that all inputs and outputs can be varied at the discretion of management or other users. In any realistic situation, however, there may exist ”exogenously fixed” or non-discretionary factors that are beyond the control of a DMU’s management, which also need to be considered. Also DEA requires that the data for all discretionary inputs and outputs be known exactly. The aim of this paper is measuring the relative efficiency of decision making units with non-discretionary inputs and interval discretionary data.
Keywords: DEA, Interval data, Non-Discretionary inputs, Efficiency
1
Introduction
Data envelopment analysis (DEA) is a mathematical programming approach for measuring and evaluating the relative efficiency of peer decision making units (DMUs) with multiple inputs and multiple outputs (Cooper et al., 2000). Discretionary models of DEA assume that all data are discretionary, i.e., controlled by the management of each DMU and varied at its discretion. In real world situations, however, there may exist ”exogenously fixed” or nondiscretionary factors that are beyond the control of a DMU’s management, which also need to be considered. Banker and Morey (1996a) developed the first model for evaluating DEA efficiency with ”exogenously fixed” inputs and
1
Corresponding author:Farhad Hosseinzadeh Lotfi, E-mail:hosseinzadeh lotfi@yahoo.com
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outputs in forms like ”age of store” in an analysis of a network of fast-food restaurants (See Ray 1991; also Roggiero 1996; Roggiero 1998; Mu˜ iz, 2006). n Some examples of
Non-Discretionary Factors and Imprecise Data in DEA
F. Hosseinzadeh Lotfi,1 and G. R. Jahanshahloo Dept. of Math., Science and Research Branch Islamic Azad University, Tehran 14515-775, Iran M. Esmaeili Dept. of Math., Islamic Azad University, Shahrekord P.O. Box 166, Shahrekord, Iran
Abstract Discretionary models of data envelopment analysis (DEA) assume that all inputs and outputs can be varied at the discretion of management or other users. In any realistic situation, however, there may exist ”exogenously fixed” or non-discretionary factors that are beyond the control of a DMU’s management, which also need to be considered. Also DEA requires that the data for all discretionary inputs and outputs be known exactly. The aim of this paper is measuring the relative efficiency of decision making units with non-discretionary inputs and interval discretionary data.
Keywords: DEA, Interval data, Non-Discretionary inputs, Efficiency
1
Introduction
Data envelopment analysis (DEA) is a mathematical programming approach for measuring and evaluating the relative efficiency of peer decision making units (DMUs) with multiple inputs and multiple outputs (Cooper et al., 2000). Discretionary models of DEA assume that all data are discretionary, i.e., controlled by the management of each DMU and varied at its discretion. In real world situations, however, there may exist ”exogenously fixed” or nondiscretionary factors that are beyond the control of a DMU’s management, which also need to be considered. Banker and Morey (1996a) developed the first model for evaluating DEA efficiency with ”exogenously fixed” inputs and
1
Corresponding author:Farhad Hosseinzadeh Lotfi, E-mail:hosseinzadeh lotfi@yahoo.com
238
F. Hosseinzadeh Lotfi et al.
outputs in forms like ”age of store” in an analysis of a network of fast-food restaurants (See Ray 1991; also Roggiero 1996; Roggiero 1998; Mu˜ iz, 2006). n Some examples of
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