基于RVM的汽车服务商维修服务质量评价方法

摘要

Aiming at the real performance appraisal requirement of comprehensive performance evaluation for maintenance and service quality at service stations (including 4S shops) which belong to automobile manufacturing enterprise,we use the relevance vector machine theoretical model algorithm to implement the evaluation of maintenance and service quality.Relevant experiments show that the relevance vector machine is better than the traditional artificial neural network,support vector machine and depth neural network for the evaluation of service quality of automobile service providers.This evaluation method improves the validity,timeliness and satisfaction of the fault treatment,and provides a good service provider for the after-sales service department of the automobile manufacturer.It provides a reference for the customers who are in good agreement with the cause of the failure.At the same time,the selection of evaluation indexes is more comprehensive and detailed,which makes the evaluation model more conducive to practical production applications.%针对汽车制造企业旗下服务站(4S店)维修服务质量业务综合绩效考核的实际需求,利用相关向量机理论模型算法,实现汽车服务商维修服务质量评价.相关实验表明采用相关向量机用作汽车服务商维修服务质量评价效果优于传统人工神经网络、支持向量机、深度神经网络等算法.该评价方法提高了故障处理的有效性、及时性和满意度,为汽车制造厂售后服务部遴选优质服务商,为客户提供契合故障原因的特性服务商提供参考.同时,更加全面、更加细致地进行了评价指标的选取,使得评价模型更有利于实际生产应用.