Data envelopment analysis: An efficient duo linear programming approach

Saber Saati, Adel Hatami-Marbini, Madjid Tavana

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Data envelopment analysis (DEA) is a powerful mathematical method that utilises linear programming (LP) to determine the relative efficiencies of a set of functionally similar decision-making units (DMUs). Evaluating the efficiency of DMUs continues to be a difficult problem to solve, especially when the multiplicity of inputs and outputs associated with these units is considered. Problems related to computational complexities arise when there are a relatively large number of redundant variables and constraints in the problem. In this paper, we propose a three-step algorithm to reduce the computational complexities and costs in the multiplier DEA problems. In the first step, we identify some of the inefficient DMUs through input-output comparisons. In the second step, we specify the efficient DMUs by solving a LP model. In the third step, we use the results derived from the second step and another LP model to obtain the efficiency of the inefficient DMUs. We also present a numerical example to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.

Original languageEnglish
Pages (from-to)90-103
Number of pages14
JournalInternational Journal of Productivity and Quality Management
Volume7
Issue number1
DOIs
Publication statusPublished - 27 Dec 2011
Externally publishedYes

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