An Inference Method for Personalized Automotive Service Based on Rough Set and Evidential Reasoning.

With the increasing development of China's automobile market, the automotive service profits have become a major part of the industry's profits. However, the after-sale service is still on passive service mode. This mode has some limitations, such as the low service quality of the recommended service items and the lack of personalized service, which seriously affect the quality of the automotive service. In order to solve problems, such as lack of personalized service in the current automotive service mode, an inference method for personalized automotive service based on rough set and evidential reasoning was proposed. First, the information entropy reduction algorithm was used to reduce the customer's driving behavior attributes, and then, the attributes that affected the status of the major components of the automobile significantly were used as evidence. Second, the weight of evidence was measured by the calculation algorithm of attributes importance. Third, the customer's personalized service requirements were inferred by the evidence synthesis algorithm. Finally, the method's effectiveness was verified by the service data of automotive brake system of an automotive service provider from FAW-Volkswagen. Results demonstrate that the rough set method can effectively extract the attributes that have important influence on customer's personalized service requirements from many customer driving behavior attributes as reasoning evidence, the belief degree of the personalized service requirements of all samples can be calculated by using the evidential reasoning method, and the minimum and the average difference between the maximum and the second largest belief degrees are larger than 0.2. These findings indicate that customer's personalized service requirements can be inferred by the method effectively. The proposed method provides a new way for personalized service requirements inference in the filed of automotive service. [ABSTRACT FROM AUTHOR]