Context-Dependent Data Envelopment Analysis with Interval Data

Abstract

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and Output) are often not so deterministic, therefore they usually are imprecise data such as interval data, hence the DEA models becomes a nonlinear programming problem and is called imprecise DEA (IDEA). In this paper the context-dependent DEA models for DMUs with interval data is extended. First, we consider each DMU (which has interval data) as two DMUs (which have exact data) and then, by solving some DEA models, we can find intervals for attractiveness degree of those DMUs. Finally, some numerical experiment is used to illustrate the proposed approach at the end of paper.

The performance measurement of listed companies of the agribusiness sector on the stock exchange of Thailand

“…There are some observations that the performance of efficient DMUs doi: 10.17221/291/2015-AGRICECON is affected by the presence of inefficient DMUs. Thus, the context-dependent DEA approach is used to solve this problem by excluding the efficient DMUs from the future DEA runs (Izadikhah 2011).…”