Two-Sided Matching Decision Making Methods Based On Preference Ordinal Information

There are some two-sided mantching decision problems in many fields of areas of decision-sided matching problems, such as the problem of marriage matching, the problem of matching medical school graduates and hospital, the problem of matching buyers with sellers in electronic business activities, the problems of matching companies and underwriters in initial public offerings of companies. The purpose of this thesis is to make a research on the two-sided matching decision problem based on preference ordinal information. Main research results of this thesis are conducted as follows:(1) The four basic principles to resolve the two-sided matching decision problem are proposed. By two examples’s contrast, we consider maximizing the sum of agents’ satisfactions and minimizing the difference of agents’ satisfactions to establish an optimization model based on the original multi-objective optimization model. Then the solution of this new optimization model are given.(2) Based on the agents’ complete preference ordinal information, the agents’ preference ordinals are transformed into satisfactions by using a kind of function which is about agents’psychological characteristics. A two-sided matching optimization model which considers the consistency of agents’ satisfactions is established and the general steps to solve the two-sided matching problem with complete preference ordinal information are given. A matching problem is given to illustrate the feasibility and validity of the proposed model.(3) Based on the agents’uncertain preference ordinal information, the agents’preference ordinals are transformed into satisfactions by using a kind of function which is about agents’preferences. A two-sided matching optimization model which considers the consistency of agents’satisfactions is established and the general steps to solve the two-sided matching problem with uncertain preference ordinal information are given. A matching problem is given to illustrate the feasibility and validity of the proposed model.