A Credibility Approach on Fuzzy Slacks Based Measure (SBM) DEA Model

TL;DR

This paper introduces a credibility-based approach to fuzzy SBM DEA models, transforming fuzzy data into crisp models at various credibility levels to better evaluate decision-making units' efficiency in uncertain environments.

Contribution

It proposes a novel credibility measure method to convert fuzzy SBM DEA models into crisp linear programming models, enhancing real-world applicability.

Findings

Fuzzy DEA model results are more aligned with real-world data.

Application to Indian oil refineries demonstrates the model's effectiveness.

The approach improves efficiency assessment under uncertainty.

Abstract

Data Envelopment Analysis (DEA) is a multi-criteria technique based on linear programming to deal with many real-life problems, mostly in nonprofit organizations. The slacks-based measure (SBM) model is one of the DEA model used to assess the relative efficiencies of decision-making units (DMUs). The SBM DEA model directly used input slacks and output slacks to determine the relative efficiency of DMUs. In order to deal with qualitative or uncertain data, a fuzzy SBM DEA model is used to assess the performance of DMUs in this study. The credibility measure approach, transform the fuzzy SBM DEA model into a crisp linear programming model at different credibility levels is used. The results came from the fuzzy DEA model are more rational to the real-world situation than the conventional DEA model. In the end, the data of Indian oil refineries is collected, and the efficiency behavior of the companies obtained by applying the proposed model for its numerical illustration.