Construction And Optimization Of Reverse Logistics Network For End-of-life Vehicles In Uncertain Environment

Along with the automobile industry fast development,the car ownership in China is increasing rapidly.At the same time,the number of end-of-life vehicles(ELVs)is increasing due to the life cycle limitation.Reverse logistics of ELVs is the requirement of circular economy and sustainable development.However,the low recovery rate of ELVs and poor management have caused serious environmental pollution and waste of resources.The reasonable design of reverse logistics network is an important method to solve this problem.It can reasonably allocate ELVs and their parts,reduce the cost of the network,improve the level of recycling,and promote the reuse,recycling and environmental protection of resources.However,due to uncertain factors,it is more difficult to build reverse logistics network.Based on the related theories of reverse logistics of ELVs,this paper forecasts the number of ELVs recovered,constructs the general model of reverse logistics network of ELVs and the model under uncertain environment.And through scenario analysis and sensitivity analysis,the relevant influencing factors of the network are deeply studied.The main contents and contributions of this paper include the following three aspects:(1)Research on prediction of recovery of ELVs based on GM(1,1)-TES-GM(1,N)-Markov combination modelRecovery prediction is an important topic to solve the uncertainty of recovery.In this paper,combined with grey model,exponential smoothing method and Markov model,a combined prediction model of ELVs recovery is constructed,which effectively deals with the influence of randomness,nonlinearity and many fluctuation factors on the prediction accuracy.Compared with the traditional single prediction method,the prediction accuracy is improved.Taking Jiangsu Province as an example,this paper forecasts the spatial distribution of the recovery volume of endof-life vehicles from 2021 to 2035.(2)Research on general optimization model of reverse logistics network for ELVsIn this paper,a multi-level reverse logistics network of ELVs is constructed,and a mixed integer linear programming(MILP)model is proposed,taking into account the recovery amount of different vehicles types,the proportion of parts and materials and processing capacity of various facilities.Taking Jiangsu Province as an example,Lingo is used to solve the problem,and the minimum network cost,optimal facility location and logistics allocation scheme are obtained.(3)Research on reverse logistics network model of ELVs in uncertain environmentIn this paper,based on the general model,considering the uncertainty of the recovery quantity of ELVs and the treatment capacity of facilities,a fuzzy mixed integer nonlinear programming(FMINLP)model is constructed by using the fuzzy programming method.Taking Jiangsu Province as an example,the improved particle swarm optimization algorithm(PSO)is used to solve the model,and the optimal solution under uncertain environment is obtained.In the sensitivity analysis,the influence of confidence level on total cost,location and allocation scheme is discussed.It is found that with the increase of confidence level,the total cost increases linearly and the demand for facilities increases.The higher the confidence level is,the more obvious the influence is.