A Benchmarking Approach with Missing Values Using Data Envelopment Analysis for Non-Symmetrical Fuzzy Data

Abstract

This study proposes a benchmarking approach where the missing data point denoted by fuzzy numbers and aims to outstretch the crisp Data Envelopment Analysis (DEA) model in a fuzzy system. The fuzzy DEA integrated model is capable for benchmarking of homogeneous decision making units (DMUs) with the missing data as non-symmetrical fuzzy data in terms of fuzzy efficiency. A numerical illustration is presented as a case study to explain the integrated fuzzy DEA model.