An outranking method for multicriteria decision making with probabilistic hesitant information. Issue 3 (29th January 2020)

• Record Type: Journal Article
• Authors: Li, Jian; Chen, Qiongxia
• Other Names: Saen Reza Farzipoor guestEditor.; Song Malin guestEditor.; Fisher Ron guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-29
• Subjects: ELECTRE -- Hausdorff distance -- multicriteria decision making -- outranking relation -- probabilistic hesitant fuzzy set; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12513
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Defects of hesitant fuzzy set (HFS) manifest in actual decision‐making process, so adding probabilities to the values in HFS is necessary. The probabilistic HFS (PHFS) is a useful tool to describe the uncertainty of elements in HFS by introducing occurrence probabilities. However, some important issues in PHFS utilization remain to be addressed. In this study, an outranking method for multicriteria decision making (MCDM) with probabilistic hesitant information is presented. First, the binary relations between two probabilistic hesitant fuzzy elements (PHFEs) are defined on the basis of the elimination and choice translating reality method. Some outranking relations between the alternatives are then introduced. Second, we provide a Hausdorff distance between two PHFEs. The main characteristic of the proposed Hausdorff distance is that it does not require the same length and arrangement of the PHFEs. Third, a maximizing Hausdorff distance deviation method is developed to obtain the evaluation criteria weights under a probabilistic hesitant fuzzy environment. Finally, an illustrative example in conjunction with comparative analysis is used to demonstrate that the proposed method is feasible for practical MCDM problems.