Performance evaluation of DMUs using hybrid fuzzy multi-objective data envelopment analysis

TL;DR

This paper introduces a hybrid fuzzy multi-objective DEA method for evaluating and ranking decision-making units, incorporating both optimistic and pessimistic perspectives, demonstrated through an educational sector case study.

Contribution

It develops fuzzy multi-objective optimistic and pessimistic DEA models and a novel ranking approach that combines both efficiencies for improved performance evaluation.

Findings

The proposed models effectively evaluate DMU performance.

The ranking approach provides a comprehensive efficiency assessment.

Application to education sector demonstrates practical utility.

Abstract

The objective of this paper is to evaluate the performance of decision-making units (DMUs) using a hybrid fuzzy multi-objective (FMO) data envelopment analysis (DEA) approach. This study develops fuzzy multi-objective optimistic (FMOO) and pessimistic (FMOP) DEA models for performance evaluation of DMUs. To rank the DMUs, a ranking approach is used that can simultaneously integrate optimistic and pessimistic efficiencies. A comparison of the proposed approach with the existing approach is made with the help of an example. Finally, a real-life application of developed fuzzy optimistic and pessimistic DEA models in the education sector is presented.