Fair and stable matching decision-making with multiple hesitant fuzzy elements

Abstract

The two-sided matching (TSM) decision-making is an interdisciplinary research field encompassing management science, behavioral science, and computer science, which are widely applied in various industries and everyday life, generating significant economic and social value. However, in the decision-making process of real-world TSM, the complexity of the decision-making problem and environment lead to the preference information provided by the two-sided agents being ambiguous and uncertain. The purpose of this study is to develop a new fair and stable matching methodology to resolve the TSM problem with multiple hesitant fuzzy element (HFE) information. The decision-making process is as follows. First, the TSM problem with four kinds of HFEs is described. To solve this problem, the HFE value of each index is normalized and then is transformed into the closeness degree by using the bidirectional projection technology. Second, based on the closeness degree, the weight of each index is calculated by using the Critic method. Then, the agent satisfaction is obtained by aggregating the closeness and the weights. Next, a fair and stable TSM model to maximizing agent satisfactions under the constraints of one-to-one stable matching is constructed. The best TSM scheme can be obtained by solving the TSM model. Finally, an example of logistics technology cooperation is provided to verify the effectiveness and feasibility of the presented model and methodology. The proposed methodology develops a novel fuzzy information presentation tool and constructs a TSM model considering the fairness and stability, which is of great significance to investigate the TSM decision-making and the resolution of real-life TSM problems under the uncertain and fuzzy environments. One future research direction is to consider multiple psychological and behavioral factors of two-sided agents in TSM problems.