Relation Between Imprecise DESA and MOLP Methods

Document Type : Research Paper

Authors

Department of Mathematics, Kerman Branch, Islamic Aazd University, Kerman, Iran

Abstract

It is generally accepted that Data Envelopment Analysis (DEA) is a method for indicating efficiency. The DEA method has many applications in the field of calculating the relative efficiency of Decision Making Units (DMU) in explicit input-output environments. Regarding imprecise data, several definitions of efficiency can be found. The aim of our work is showing an equivalence relation between one of the models of DEA with imprecise data and Multiple Objective Linear Programming (MOLP). The relation between DEA and MOLP was studied to use interactive multiple objective models for solving the DEA problem in exact situation and find the most preferred solution. The aim of this study is to analyze an equivalent relation between imprecise DEA (IDEA) and MOLP models. In this context, we tried to solve IDEA models with interactive project procedure. The Project method is the responsible method, because it can estimate any efficient solution, and it indicates Most Preferred Solution (MPS). In addition, we will use the Data Envelopment Scenario Analysis (DESA) model. The main characteristic of DESA model is to decrease all inputs and increase all outputs and estimate one problem instead of n problems.

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References

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.
Cooper, W. W., Park, K. S., & Yu, G. (1999). IDEA and ARIDEA: Models for dealing with imprecise data in DEA. Management Science, 45, 597-607.
Ebrahimnejad, A., & Tavana, M. (2014). An interactive MOLP method for identifying target units in output-oriented DEA models: The NATO enlargement problem. Measurement, 52, 124-134.
Luque, M., Yang, J., & Wong, B. Y. (2009). PROJECT Method for multiobjective optimization based on gradient projection and reference points. IEEE Transactions on systems, Man, and Cybernetics-part A: System and Humans, 39(4), 864-879.
Luque, M., Lopez-Agudo, L. A., & Marcenaro-Gutierrez, O. D. (2015). Equivalent reference points in multiobjective programming. Expert Systems with Applications, 42, 2205-2212.
Thanassoulis, A. D., & Dyson, R. G. (1992). Estimation preferred target input-output levels using data envelopment analysis. European Journal of Operational Research, 56, 80-97.
Tavana, M., Ebrahimnejad, A., Santos-Arteaga, F. J., Mansourzadeh, S. M., & Matin, R. K. (2017). A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia, Socio-Economic Planning Sciences (In Press).
Yang, J. B., & Xu, D. L. (2014). Interactive minimax optimization for integrated performance analysis and resource planning. Computer & Operations Research, 46, 78-90.
Wang, Y. M., Greatbanksa, R., & Yang, J.B. (2005). Interval efficiency assessment using data envelopment analysis fuzzy sets. Fuzzy Sets and Systems,  153, 347-370.
Wong, B. Y., Yang, J. B., & Greatbank, R. (2004). Using DEA and ER approach for performance measurement of UK retail banks. MCDM 2004, Whistler, Canada.
Interdisciplinary Journal of Management Studies
Volume 11, Issue 1
January 2018
Pages 23-36

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