Evaluation Method Of Cars Based On Multi-attribute Group Decision Making

With the improvement of people’s living standards, as well as the accelerated pace of life, the private car has become a necessary tool for a lot of people going outside. At the same time, the rapid development of the network makes people have more and more comments on the car. However, due to the uncertainty and ambiguity of human thinking, or because the computational cost is too high, decision-makers are difficult to give specific assessment information on an attribute of the car from the current comments. Because vague language is the expression of the human uncertainty and fuzziness of thinking, language assessment has become the first choice of the decision-makers when they are giving evaluation about the car. Attribute weights reflect the different reviewers focus on the properties, so the objective weight calculation is also one of the subjects of the study.Thinking about that imprecision and ambiguity for the study provides a good framework, this paper will be based on fuzzy linguistic assessment of decision-makers of car attributes to calculate the objective weight of the properties with the use of fuzzy numbers based on the idea of the TOPSIS method With the eventual adoption of the multi-attribute group decision-making on a variety of private cars to a comprehensive evaluation, the evaluation results will be the purchase of reference when you choose models and automobile manufacturers based on the evaluation results have a better grasp on the future direction of development.The main research works are as follows:(1) The weight calculation of vehicle attributes Based on TOPSIS ideological.In order to reflect the size of different attributes of the car that the reviewers focus on and facilitate manufactuers to take advantage of the psychological tendency of consumers to create models which consumers are satisfied with. This paper uses the language evaluation value of the candidate vehicle attributes given by the dicision makers to structure a model based on the TOPSIS ideological. This model makes square between the value of weighted attribute of the assessed car and the biggest value of the same attribute of all the cars to a minimum and makes square between the value of weighted attribute of the assessed car and the smallest value of the same attribute of all the cars to a maximum. The weight values can be obtained by soving the above model. The results show that the weights are in line with the evaluators psychological, which proves that the validity of the method is feasible.(2) Standardization of different granularity linguistic assessment set.Different decision-makers on the same evaluation of the object may take a different set of granularity linguistic assessment. On this situation, this paper establishes a basic language evaluation set whose granularity is7, and creates a mapping based on the relationship between fuzzy numbers to convert evaluation given by the three decision makers using own set of evaluation to evaluation vector based on the basic set. And then that makes the harmonization of different granularity linguistic assessment matrix.(3) Giving the car ranking sequence by building up the evaluation.This paper assembles Evaluation information of multiple decision makers by MEOWA operator and then assembles the attribute information of each car by the same way and finally converts the fuzzy number for the exact value by defuzzification algorithm, which gives the car a rankings sequence. This sequence is consistent with the pre-survey results.