Abstract

This paper processes a unification of Fuzzy TOPSIS and Data Envelopment Analysis (DEA) to select the units with most efficiency. This research is a two‐stage model designed to fully rank the organizational alternatives, where each alternative has multiple inputs and outputs. First, the alternative evaluation problem is formulated by Data Envelopment Analysis (DEA) and separately formulates each pair of units. In the second stage, we use the opinion of experts to be applied into a model of group Decision‐Making (DM) called the Intuitionistic Fuzzy TOPSIS (IFT) method. The results of both methods are then multiplied to obtain the results. DEA and Intuitionistic Fuzzy TOPSIS ranking do not replace the DEA classification model; rather, it furthers the analysis by providing full ranking in the DEA context for all units by aggregate individual opinions of decision makers for rating the importance of criteria and alternatives.

Highlights

  • Data Envelopment Analysis DEA measuring the relative efficiency of peer decision-making units DMUs with multiple inputs and multiple outputs was introduced by Charnes et al 1

  • The ratings of each alternative, which were given with Intuitionistic fuzzy information, were represented as IFNs

  • The IFWA operator was used to aggregate the rating of decision makers

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Summary

Introduction

Data Envelopment Analysis DEA measuring the relative efficiency of peer decision-making units DMUs with multiple inputs and multiple outputs was introduced by Charnes et al 1 This method is based on linear programming LP , which gives it the ability to measure the decision units in a relative manner, though it has difficulties in measuring different scales and more than one scale, as well as in comparing entries or outputs that are in different units. Whilst the MCDM literature does not consider a complete ranking as their ultimate aim, they do discuss the use of preference information to further refine the discriminatory power of the DEA models. In this manner, the decision-makers could specify which inputs and outputs should lend greater importance to the model solution

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