Undesirable Outputs’ Presence in Centralized Resource Allocation Model

Ghasem Tohidi,Hamed Taherzadeh,Sara Hajiha

doi:10.1155/2014/675895

Ghasem Tohidi, Hamed Taherzadeh + Show 1 more

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https://doi.org/10.1155/2014/675895

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Abstract

Data envelopment analysis (DEA) is a common nonparametric technique to measure the relative efficiency scores of the individual homogenous decision making units (DMUs). One aspect of the DEA literature has recently been introduced as a centralized resource allocation (CRA) which aims at optimizing the combined resource consumption by all DMUs in an organization rather than considering the consumption individually through DMUs. Conventional DEA models and CRA model have been basically formulated on desirable inputs and outputs. The objective of this paper is to present new CRA models to assess the overall efficiency of a system consisting of DMUs by using directional distance function when DMUs produce desirable and undesirable outputs. This paper initially reviewed a couple of DEA approaches for measuring the efficiency scores of DMUs when some outputs are undesirable. Then, based upon these theoretical foundations, we develop the CRA model when undesirable outputs are considered in the evaluation. Finally, we apply a short numerical illustration to show how our proposed model can be applied.

Highlights

Data envelopment analysis (DEA) was introduced in 1978
The existing centralized resource allocation (CRA) models have been focused on desirable inputs and outputs
We developed an approach proposed by Lozano and Villa [15] for dealing with undesirable outputs by using distance directional function

Summary

Introduction

DEA includes many models for assessing the efficiency score in the variety of conditions. Many researchers use this technique to evaluate the efficiency and inefficiency scores of decision making units (DMUs). Two of the most common DEA models are CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) which were introduced by Charnes et al [1] and Banker et al [2], respectively. Classical DEA models (such as CCR, BCC, ADD, and SMB) rely on the assumption that inputs have to be minimized and outputs have to be maximized. Classical DEA models need to be modified in order to deal with the situation because undesirable outputs should not maximize at all

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