A systematic fuzzy multi-criteria group decision-making approach for alternatives evaluation. Issue 9 (2nd September 2019)

• Record Type: Journal Article
• Authors: Peng, Jian-Gang; Xia, Guang
• Is Part Of: Journal of the Operational Research Society. Volume 70:Issue 9(2019)
• Page Start: 1490
• Page End: 1501
• Publication Date: 2019-09-02
• Subjects: Hesitant fuzzy sets -- decision analysis -- investment -- multi-criteria group decision-making -- linguistic term sets; Operations research -- Periodicals; 658.4034
• DOI: 10.1080/01605682.2018.1495995
• Languages: English
• ISSNs: 0160-5682

Abstract

Abstract: Given that the values of the criteria in uncertain multi-criteria group decision-making (MGDM) problems take the form of fuzzy linguistic variables, this paper proposes a model based on hesitant fuzzy linguistic term sets (HFLTSs), named MGDM-HFLTS, to estimate investment alternatives for angel investors. To meet the challenges of complexity, lack of information and time pressure among several possible values in MGDM, the HFLTSs are introduced and revised. The HFLTSs, which are convenient and sufficiently flexible to reflect the decision-makers' preferences, are introduced to represent the hesitation or doubt originating from systematic comparisons of the assessment values of alternatives for each criterion during both preference elicitation and alternative evaluation phases. Then, context-free grammar is revised for computing with words to enhance and extend the applicability of HFLTSs according to a set of various membership degrees over which decision-makers hesitate when eliciting their preferences over alternatives. Subsequently, the most satisfactory alternative(s) is/are determined by the outranking relationship approach to integrate the degree of preference and entropy information. In addition, studies of evaluation criteria and their weights in angel investment decision-making are investigated. An illustrative example of an angel investment implemented by the proposed MGDM-HFLTS and its corresponding algorithm confirms the effectiveness and practicability of the proposed method.