An inverse optimization model for imprecise data envelopment analysis

A. Hadi-Vencheh, A. Hatami-Marbini, Z. Ghelej Beigi, K. Gholami

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Inverse data envelopment analysis (InDEA) is a well-known approach for short-term forecasting of a given decision-making unit (DMU). The conventional InDEA models use the production possibility set (PPS) that is composed of an evaluated DMU with current inputs and outputs. In this paper, we replace the fluctuated DMU with a modified DMU involving renewal inputs and outputs in the PPS since the DMU with current data cannot be allowed to establish the new PPS. Besides, the classical DEA models such as InDEA are assumed to consider perfect knowledge of the input and output values but in numerous situations, this assumption may not be realistic. The observed values of the data in these situations can sometimes be defined as interval numbers instead of crisp numbers. Here, we extend the InDEA model to interval data for evaluating the relative efficiency of DMUs. The proposed models determine the lower and upper bounds of the inputs of a given DMU separately when its interval outputs are changed in the performance analysis process. We aim to remain the current interval efficiency of a considered DMU and the interval efficiencies of the remaining DMUs fixed or even improve compared with the current interval efficiencies.

Original languageEnglish
Pages (from-to)2441-2454
Number of pages14
JournalOptimization
Volume64
Issue number11
Early online date6 Nov 2014
DOIs
Publication statusPublished - 2 Nov 2015
Externally publishedYes

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