Hesitant Fuzzy Linguistic Two-sided Matching Decision Making Methods Based On Incomplete Information

In human being's daily life,there exist many situations that matching objects on one side should match matching objects on the other side,such as marriage matching,college admissions,person-job matching,knowledge service matching and so on.So far,research on two-sided matching problems has achieved widespread achievement with the study of both domestic and international scholars.In real two-sided matching problems,matching objects will hesitate between different linguistic terms for the reason that single linguistic term cannot express their preferences accurately.Moreover,matching objects tend to use multi-granular linguistic information to elicit their preferences due to the difference in cultural and education background.Because of the complicated matching environment and lack of knowledge,incomplete information of preferences will be provided as well.This thesis has done research on incomplete information in two-sided matching problems with multi-granular hesitant linguistic information and main research on this thesis is summarized as follows:(1)Research on multi-criteria two-sided matching problems with multi-granular HRLTSs and incomplete criteria weight information is conducted,considering the condition that matching objects cannot consider the priority between criteria when eliciting preference information and provide incomplete criteria weight.First,for those matching objects who provide incomplete weight information,optimal criteria weight vectors for matching objects are conducted with maximizing deviation method by calculating the deviation under each criterion according to the linguistic information given by matching objects on two sides as well as the incomplete weight information.After deriving the optimal criteria weight vectors for each matching object,the aggregated multi-granular linguistic distribution assessments are calculated and further unified to the basic linguistic term set.The satisfaction degrees for matching objects on both sides are then derived and the matching model which aims to maximize the overall satisfaction degrees of matching objects on both sides is conducted.By solving the model,the optimal stable matching is derived.Finally,an illustrated example on green building technology supply and demand two-sided matching is demonstrated.(2)Research on two-sided matching problems with multi-granular incomplete HFLPRs is conducted,considering the condition that matching objects will elicit incomplete HFLPRs when comparing two objects and having little knowledge with each other.First,an optimization model which aims to minimize the overall adjustments and to fill the incomplete HFLPRs and improve the consistency of the HFLPRs is built.Based on the proposed model,incomplete HFLPRs elicited by matching objects on both sides are adjusted and complete additively HFLPRs are derived,which reach the best additive consistency and average additive consistency.After deriving the complete additively HFLPRs,aggregated multi-granular linguistic assessments are calculated and further unified to the basic linguistic term set.The satisfaction degrees for matching objects on both sides are then calculated and the matching model which aims to maximize the overall satisfaction degrees of matching objects on both sides is conducted.By solving the model,the optimal stable matching result is derived.Finally,an example of person-job matching is provided to illustrate the proposed method.The two-sided matching methods proposed by this thesis comprehensively solve the problems that involve multi-granular hesitant fuzzy linguistic information and incomplete information,which has a guiding effect on actual two-sided matching problems.