A systematic fuzzy multi-criteria group decision-making approach for alternatives evaluation

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.

Keywords:

• Hesitant fuzzy sets
• decision analysis
• investment
• multi-criteria group decision-making
• linguistic term sets

Acknowledgements

This research was funded by the National Key Research and Development Program of China grant numbers 2016YFD0700604 and 2016YFD0700605; sponsored by the Fundamental Research Funds for the Central Universities in China grant number JZ2016HGBZ1035; and supported by Anhui University Natural Science Research Project grant number KJ2017A891.

Disclosure statement

No potential conflict of interest was reported by the authors.