Study Of Two-sided Matching Decision Problem Based On Probabilistic Uncertain Linguistic Information

Two-sided matching problem is widespread in daily life and is currently a popular research topic.For example,the matching of men and women in marriage,the matching of people and jobs in human resources,the matching of companies and investors in investment platforms,the matching of supply and demand in operators,etc.Due to the complexity of the real-world matching environment and the limitations of the matching subject’s knowledge,there is a general level of uncertainty and hesitation when people give preference information.Therefore,scholars have paid extensive attention to two-sided matching problem in an uncertain information environment,and a large number of research results have emerged.However,few research results have been published on the two-sided matching problem under probabilistic uncertain linguistic term sets.In view of this,this thesis investigates the two-sided matching problem based on probabilistic uncertain linguistic information and proposes a targeted two-sided matching method.For the two-sided matching problem under probabilistic uncertain linguistic information,the priority-based two-sided matching decision method is proposed by fully considering the psychological perception of two-sided matching subjects.To avoid the distortion and twist of information caused by traditional methods,interval-type scores are first defined to handle probabilistic uncertain linguistic term sets preference information.Second,the formula for the possible degree of PULTS is given,and its properties are investigated,based on this,the priority function is constructed.Further,the satisfaction matrix is obtained based on the priority and distance measures.Finally,the feasibility and effectiveness of the matching decision method are verified by arithmetic examples and comparative analysis.For the two-sided matching problem dominated by intermediaries under probabilistic uncertain linguistic information,a two-sided matching optimization model is constructed with the objectives of maximizing the satisfaction of two-sided matching subjects and maximizing the income of intermediaries by considering the income of the intermediary platform.Firstly,credibility is defined to measure the reliability of matching subject preference information,and then the formula for calculating the PULTS score is given based on the credibility.Secondly,the intermediary platform cost function is given and its function is normalized,Then,the satisfaction matrix is obtained by the improved TODIM method.based on which the two-sided matching optimization model is constructed with maximizing the satisfaction of firm-investor matching and maximizing the revenue of the intermediary platform as the objectives.Finally,an investment and financial platform is used as an example and a comparative analysis is performed to clarify the feasibility and effectiveness of the proposed method.