A segment-based approach to the analysis of project evaluation problems by hesitant fuzzy sets

Abstract

We provide a methodology to perform an extensive and systematized analysis of problems where experts voice their opinions on the attributes of projects through a hesitant fuzzy decision matrix. This provides the decision-maker with ample information on which he or she can rely in order to make the final decision, in the form of segments instead of numbers. These segments derive from weighted average of new parametric expressions for two tenable indices of satisfaction, the distance to an ideal or the similarity to an anti-ideal, and permit to give a profuse unified picture of the relative performance of the projects. When the parameter grows, these indices tend to replicate the evaluation by respective simplistic expressions that only depend on the least, resp., the largest, evaluation and the number of evaluations in each cell.

Decision Making with Uncertainty Using Hesitant Fuzzy Sets

“…For example, Zhang and Wei extended VIKOR and TOPSIS methods [52], whereas ELECTRE extensions with HFS are presented in [6,25]. However, HFS has been also used to provide the new methodology, e.g., a segmentbased approach [1]. It confirms the fact that HFS is a very useful tool to deal with uncertainty.…”

Soft Computing Under Uncertain Knowledge

“…Along with the studies by Rodríguez et al [81] and Xu [99], there are many other recent papers on the theoretical approaches on hesitant fuzzy sets and their practical applications, which show the importance of hesitant fuzzy elements and sets. Alcantud introduced an approach to analyze projects characterized by hesitant fuzzy sets by relating hesitant fuzzy sets to other soft computing models [13]. Other papers on hesitant sets are [2,15,16,20,66,67,70] The theory of rough sets deals with vagueness from a different point of view [14].…”

Dual Extended Hesitant Fuzzy Sets

“…Hesitant fuzzy sets (HFSs) use many-valued sets of membership degrees [11][12][13][14]. Real applications validate this model, and decision-making approaches of various forms permit to act flexibly with data under hesitancy [15]. If we can also avail ourselves of hesitant information on non-membership degrees, then dual hesitant fuzzy sets (DHFSs) provide a natural extension of both HFSs and intuitionistic fuzzy sets.…”