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Determining the efficiency of decision-making units using the technique of data envelopment analysis in the presence of dual-role performance factors

Document Type : Research Paper

Authors

1 Department of Mathematics, Islamic Azad University, Sirjan Branch, Sirjan, Iran

2 Associate Professor, Faculty of Administrative Sciences and Economics Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

3 Master of Industrial Engineering, Faculty of Administrative Sciences and Economics, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

10.22054/jims.2024.80057.2920

Abstract

Data Envelopment Analysis (DEA) is an approach based on mathematical programming for the relative evaluation of decision-making units treated as similar yet distinct production systems. In this approach, the performance of each unit is characterized by describing the transformation of specific inputs into specific outputs. Traditional DEA models assume that the role of each performance factor is clearly defined. However, in some real-world problems, certain factors might be identified as dual-role factors depending on the evaluation nature or the decision-makers' perspective. These factors can play the role of both input and output, or even be considered neutral in assessing the units' performance. In the current paper, to determine the status of dual-role factors and calculate the efficiency of DMUs, two new linear programming models, based on the concept of deviation in the efficiency constraint and a common set of weights, are suggested. The main advantages of the proposed models are significantly reducing the computations and iterations required to solve the model, and involving all DMUs to determine the role of factors. To assess the performance of the proposed models, a data set for the evaluation of eighteen suppliers in the presence of two inputs, three outputs, and two dual-role factors has been employed. The obtained results showed that, compared to other models, the proposed models are computationally more efficient, and the role determination and evaluation of the units, based on the obtained weights from these models, are better aligned with the expectations of decision-makers

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References
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Industrial Management Studies
Volume 22, Issue 73
September 2024
Pages 143-184
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