A review of fuzzy AHP methods for decision-making with subjective judgements. (15th December 2020)

• Record Type: Journal Article
• Authors: Liu, Yan; Eckert, Claudia M.; Earl, Christopher
• Is Part Of: Expert systems with applications. Volume 161(2020)
• Publication Date: 2020-12-15
• Subjects: Fuzzy Analytic Hierarchy Process -- Fuzzy set -- Multi-criteria decision-making -- Subjective judgement -- Selection problem; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113738
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The techniques to build FAHP model are reviewed in terms of four important aspects. Four types of fuzzy sets are discussed regarding how to establish comparison matrix. Aggregation and defuzzification methods are examined for their pros and cons. Measurement methods of crisp and fuzzy consistency are compared. Guidance is provided on choosing suitable techniques; open questions are suggested. Abstract: Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work.