Resource allocation based on DEA for distance improvement to MPSS points considering environmental factors

Document Type : Research Paper

Authors

1 Faculty of Mathematics and Computer Science, Amirkabir University of Technology, 424 Hafez Avenue, 15914 Tehran, Iran

2 Department of Mathematics, Islamic Azad University, Karaj Branch, Karaj, Iran

Abstract

This paper proposes a new resource allocation model which is based on data envelopment analysis (DEA) and concerns systems with several homogeneous units operating under supervision of a central unit. The previous studies in DEA literature deal with reallocating/allocating organizational resource to improve performance or maximize the total amount of outputs produced by individual units. In those researches, it is assumed that all data are discretionary. Resource allocation problem has a multiple criteria nature; thus to solve it, many intervening factors should be regarded. This paper not only develops resource allocation plan for systems with both discretionary and non discretionary data in their inputs, but also considers environmental factors as well. In addition, the overall distance from the decision making units (DMUs) to their most productive scale size (MPSS) points is taken into account and is minimized in this method. To find the best allocation plan, this paper applies multiple objective programming (MOLP). Numerical examples are employed to illustrate the application of this approach on real data.

Keywords

References

[1] Charnes, A., Cooper,W. W. and Rhodes, E. (1978), Measuring the efficiency of decision making units. European Journal of operational Research, 2, 429-444.
[2] David A.Haas, Frederic H. Murphy. (2003) Compensating for nonhomogeneity in decision making units in data envelopment analysis, 144,530-544.
[3] Yan, H., wei, Q.L, Hao, G. (2002), DEA models for resource allocation and production input -output estimation. European Journal of operational Research, 136(1), 19-31.
International Journal of Data Envelopment Analysis
Volume 5, Issue 2
May 2017
Pages 1207-1230

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