Service Capacity Optimization Of Auto Parts Service Network Based On Inverse Optimization

In recent years,customized consumption has become a consumption trend,and the customized demand for auto parts service makes parts service enterprises more dependent on auto parts resources.However,the total amount of accessory resources in the accessory service network is limited,and there is resource competition among each node,so that the allocation of accessories parts resources in the whole service network is unbalanced.Therefore,on the premise of meeting the market demand,how to optimize and adjust the resource allocation structure of the service network and improve the overall service level of the system is an important problem that needs to be solved urgently.This paper will analyze from two levels: the whole and the local.Firstly,this paper studies the relationship of various subjects in the auto parts industry in the service network,and summarizes the characteristics of the two-level auto parts resource allocation structure including 4S shop group and multi-node 4S shop enterprises.Based on this,a bi-level planning model of 4S shop group-multi-node 4S shop parts resource allocation is constructed to optimize the allocation of auto parts resources at the upper and lower layers,and the optimal allocation scheme is obtained by particle swarm optimization algorithm,so as to realize the first positive optimization of resource allocation in the auto parts service network.On this basis,in order to respond to the dynamic market demand and meet the actual market demand,the inverse optimization method is used to rationalize the number of parts resources in the lower model,so as to obtain the resource allocation optimization scheme to meet the actual market demand.According to this scheme,lower-layer node enterprises can adjust and optimize their own parts resources by means of horizontal replenishment and group regulation,while the group will carry out a secondary overall planning based on the feedback information of lower-layer node enterprises to realize the global optimization of parts resources of the whole service network,thus improving the stability of the overall system.The results show that the resource allocation optimization model based on bi-level planning can effectively alleviate the waste of spare parts resources.When the forward optimization results of auto parts resources are difficult to meet the actual market demand,this paper adjusts the parameters of the lower original model through the inverse optimization method,so as to achieve a more rational resource allocation.The combination of the bi-level programming method and the inverse optimization method not only provides a new idea and method to solve the resource allocation problem in the auto parts service network,but also expands the application scenarios of the two theories and models,and improves the practical application value of the theoretical model.