Two-sided matching decision making with multi-granular hesitant fuzzy linguistic term sets and incomplete criteria weight information

• Published in: Expert systems with applications Vol. 168; p. 114311
• Main Authors: Zhang, Zhen, Gao, Junliang, Gao, Yuan, Yu, Wenyu
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15-04-2021; Elsevier BV
• Subjects: Assessments; Criteria; Decision making; Fuzzy sets; Green buildings; Hesitant fuzzy linguistic term set; Incomplete weight; Linguistics; Matching; Mathematical analysis; Matrix methods; Multi-granular linguistic information; Optimization; Stable matching; Two-sided matching; Weight; Incomplete weight; Multi-granular linguistic information; Two-sided matching; Stable matching; Hesitant fuzzy linguistic term set
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.114311

Two-sided matching decision making (TSMDM) problems exist widely in human being’s daily life. For practical TSMDM problems, matching objects with different culture and knowledge backgrounds usually tend to provide linguistic assessments using different linguistic term sets (i.e., multi-granular linguistic information). Moreover, for TSMDM problems with high uncertainty, it is possible that matching objects may have some hesitancy and thus provide hesitant fuzzy linguistic term sets (HFLTSs). To model these situations, an approach to TSMDM with multi-granular HFLTSs is developed in the paper. In the proposed approach, some optimization models are first constructed to determine criteria weights for matching objects who do not provide clear criteria weight vectors. Afterwards, each matching object’s hesitant fuzzy linguistic decision matrix is aggregated to obtain his/her collective assessments over matching objects on the other side, which are denoted by multi-granular linguistic distribution assessments. These multi-granular linguistic distribution assessments are unified to obtain matching objects’ satisfaction degrees. Furthermore, an optimization model which aims to maximize the overall satisfaction degree of matching objects by considering the stable matching condition is then established and solved to determine the matching between matching objects. Eventually, an example for the matching of green building technology supply and demand is provided to demonstrate the characteristics of the proposed approach. Compared with previous studies, the proposed approach allows matching objects to provide linguistic assessments flexibly and can deal with the situations when incomplete criteria weight information is provided.
•Study two-sided matching problems with hesitant fuzzy linguistic term sets.•Multi-granular hesitant fuzzy linguistic term sets are considered.•Some models are provided to determine criteria weight vectors for matching objects.•An optimization model is established to determine stable matching results.