A novel fuzzy data envelopment analysis based on robust possibilistic programming: possibility, necessity and credibility-based approaches

¹ Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
² Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

^* Corresponding author: e_mohammadi@iust.ac.ir

Received: 28 September 2016
Accepted: 23 February 2018

Abstract

Possibilistic programming approach is one of the most popular methods used to cope with epistemic uncertainty in optimization models. In this paper, several robust fuzzy data envelopment analysis (RFDEA) models are proposed by the use of different fuzzy measures including possibility, necessity and credibility measures. Despite the regular fuzzy DEA methods, the proposed models are able to endogenously adjust the confidence level of each constraints and produce both conservative and non-conservative methods based on various fuzzy measures. The developed RFDEA models are then linearized and numerically compared to regular fuzzy DEA models. Illustrative results in all of the FDEA and RFDEA models show that, maximum efficiency is obtained for possibility, credibility and necessity-based models, respectively.