Research On Multiple Choice Between Suppliers And Customers In E-commerce Environment

With the development of the technology and science,the original way to solve match problem between the sellers and the buyers are not adapt to the new change.The shopping mode via Internet is more and more general.Under this trend,the match problem between customers and suppliers with the method improving with the base of the original ones is the most urgent problem to be solved.In the past,the problems of bilateral matching are one-to-one and one-to-many matching,and the attribute weights are known,so both sides use one way to evaluate each other.In this paper,we propose a many-to-many decision making problem in which the attribute weight information is unknown and the evaluation information is expressed in preference order,interval number and language variable.It meets the requirements of different evaluation methods because of different preferences and solves the extremely special decision making problem of multi-to-many bilateral matching problems with completely different attribute values.At the same time it has wider adaptability.In this thesis,the assignment problem in operational research is taken as the classical model,and on this basis,the model for multi-direction selection problem is established.and the attribute weight value is used as the scale to express the importance of different attribute.Which transformed the two multi-objective optimization problems between customers and supplier as decision makers into single-objective optimization problems.Then make out an solution.On the phase of data example,based on the actual investigation,several basic attribute values affecting the dominance of suppliers and customers are determined.The coefficient matrix is formed by evaluating the information of one side to the other in different ways.Through three methods of conversion of evaluation information,the single point value matrix is transformed into a single point value matrix,and then the decision matrix is normalized.Finally,the genetic algorithm is used to determine the content of chromosome design,crossover and mutation design,the formula of fitness function and the selection of the optimal individual,so as to solve the optimal solution vector.The feasible solution is realized by matlab and verified by analysis simulation.Finally,the obtained value is brought into the optimal solution vector,and the comprehensive evaluation value is calculated.Finally,the order of the optimal solution is determined by the size of the comprehensive evaluation value.