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.
Highlights
Data envelopment analysis (DEA), developed by Charnes et al [1], usually evaluates decision making units (DMUs) from the angle of the best possible relative efficiency
The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others
The context-dependent DEA [2,3,4] is introduced to measure the relative attractiveness of a particular DMU when compared to others
Summary
Data envelopment analysis (DEA), developed by Charnes et al [1], usually evaluates decision making units (DMUs) from the angle of the best possible relative efficiency. Relative attractiveness depends on the evaluation context constructed from alternative DMUs. The original DEA method evaluates each DMU against a set of efficient. Despotits and Smirlis [11] calculated upper and lower bounds for the efficiency scores of the DMUs with imprecise data They developed an alternation approach for dealing with imprecise data. By introducing some models based upon these efficient frontiers we can measure the relative attractiveness and progress of these DMUs and we can determine the interval attractiveness and interval progress for each original DMU with interval data. Further by combination of these measures we can characterize the performance of DMUs. The rest of the paper is organized as follows: section introduces the basic definitions of interval data and notations of the original context-dependent DEA. Some conclusions are pointed out in the end of this paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
