Research On Multi-objective Distribution Path Of Urban Cold Chain Considering Distribution Plan

Driven by the current consumption upgrade,fresh retail enterprises have gradually adopt new retail models,integrating online and offline,thereby expanding sales channels and shortening the distribution range of the last mile.Although this approach improves the quality of service for the end consumers,it puts tremendous pressure on the replenishment process of the store.Therefore,this paper makes the replenishment process of urban B2B,Business to Business,as the main problem to study,and proposes a multi-objective multi-distribution center distribution path optimization model based on the distribution plan,and designs an adaptive genetic annealing algorithm to solve the problem,respectively finding the corresponding optimal distribution route plan for the planned and unplanned models to meet the actual demand situation of the store replenishment under the new retail mode.The main research results of this paper are as follows:(1)According to the replenishment demand situation of the store,the different objective functions of the planned and unplanned distribution models are firstly constructed,and the carbon emission cost is considered in the cost structure of the traditional cold chain VRP model,Vehicle Routing Problems,and a multi-objective multi-distribution center cold chain distribution route optimization model based on the planned and unplanned distribution is constructed,and the main objective method and the linear weighting method are used to reduce the multi-objective function,and the overall solution is used to globally optimize the multi-distribution center path problem.(2)The adaptive genetic annealing algorithm is used to solve the model,and the character set coding method and elite retention strategy and the scanning algorithm considering service priority are adopted,and the selection operator based on simulated annealing and nonlinear adaptive crossover and mutation operators are designed.By comparing with the standard genetic algorithm,it is found that the convergence speed of the algorithm is faster and the total cost of logistics and distribution is lower,and the optimization ability and solution efficiency of the algorithm are verified.(3)Taking the distribution service of two distribution centers for 22 stores as an example to solve the model,and the total cost of the planned distribution model under different satisfaction constraints is compared,and it is found that when the minimum satisfaction of each store is set to 85%,its comprehensive replenishment distribution scheme is optimal;through 20 solving and analysis,it is found that in the unscheduled distribution,the time-return model can find a better optimal solution than the traditional time model,and can determine the vehicle service order according to the size of the demand,meeting the unplanned emergency replenishment demand,verifying the feasibility of the model.There are 36 figures,16 tables,and 60 references.