Group multiple criteria ABC inventory classification using the TOPSIS approach extended by Gaussian interval type-2 fuzzy sets and optimization programs

The aim of this paper is to extend the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach with Gaussian Interval Type-2 Fuzzy Sets (GIT2FSs) as an alternative to the traditional triangular Membership Functions (MFs) in which GIT2FSs are more suitable for stating curved MFs. For this purpose, a new Limit Distance (LD) based on alpha cut is presented for prioritizing GIT2FSs. The proposed method determines the maximum and minimum reference limits of GIT2FSs as the positive and negative ideal solutions and, then, calculates distances between assessments and these limits. In addition, in order to eliminate the weights derived from the LD calculations, the weights of the quantitative and qualitative criteria are extracted using two linear programming models, separately. In order to show the effectiveness of the proposed method, a case study is exhibited on a real GMCABCIC problem, and the results are then compared with those obtained by other techniques.