Research On The Optimization Of Vehicle Routing For Electric Vehicle With Multi-Temperature Joint Distribution

With the expansion of market demand and incentives from government subsidies,electric vehicles,as a transportation tool powered by electricity,are gradually being applied in the cold chain logistics distribution field represented by fresh food and medicine due to their characteristics of protecting the environment and saving energy.To improve delivery efficiency and ensure the freshness of goods,more and more delivery vehicles are adopting a multi-temperature joint distribution method.However,in practical operation,the demand for multi-temperature joint distribution has high complexity,such as the quantity,type,temperature demand of goods,and logistics requirements in different temperature regions.At the same time,it is necessary to consider the characteristics and limitations of electric vehicles,such as battery range,charging time,etc.Therefore,the electric vehicle routing problem with multitemperature joint distribution(EVRP-MTJD)is a typical combinatorial optimization problem,which needs to comprehensively consider multiple factors,such as the temperature demand of different goods,the number of distribution vehicles,the capacity of vehicles,the distribution time and the location of the electric vehicle charging station.This article aims to comprehensively consider the above factors while minimizing distribution costs and optimizing distribution route,so that the entire distribution process can not only meet the needs of different goods,but also improve distribution efficiency and reduce carbon emissions.The main work is as follows:Firstly,through literature review,the differences between traditional cold chain logistics and multi-temperature joint distribution are compared and analyzed,and the operating mechanisms and characteristics of mechanical multi-temperature joint distribution and cold storage multi-temperature joint distribution are compared.Therefore,the use of cold storage multi-temperature joint distribution is chosen for cold chain logistics distribution.Introduce the relevant solving algorithms for vehicle routing problems,compare the advantages and disadvantages of these algorithms,and determine to use ant colony algorithm to solve the problem studied in this article.Introduce the principles,models,and processes from three aspects.Secondly,due to the uncertainty of cold chain logistics distribution,an electric vehicle routing problem with multi-temperature joint distribution and soft time windows(EVRP-MTJD-STW)is studied under the premise of considering soft time windows.Establish a mathematical model with the goal of minimizing cost under the constraints of time window,load,electricity consumption,and mileage.To effectively solve the model,a time window factor and a frozen product impact factor were added to the basic ant colony algorithm,and fused with the 2-opt algorithm to solve the Solomon example and verify the effectiveness of the algorithm and model.The research results indicate that adopting a multi-temperature joint distribution model can effectively reduce distribution costs and improve vehicle utilization,which has more advantages compared to traditional cold chain logistics distribution.In addition,as the width of the soft time window increases,the distribution center can arrange delivery services more flexibly,thereby further improving delivery efficiency.Finally,a dynamic update strategy is proposed for the vehicle routing problem with dynamic demands.Based on EVRP-MTJD,the dynamic demand based electric vehicle routing problem with multi-temperature joint distribution(DDEVRP-MTJD)is studied.By analyzing the initial stage and dynamic optimization stage,an improved ant colony algorithm is used to obtain the distribution route of the initial customer in the initial stage;In the dynamic optimization stage,a timed optimization strategy is adopted to dynamically update the dynamic customer needs,and the insertion algorithm is added to the improved ant colony algorithm to quickly adjust the distribution route.Based on the Solomon dataset R105 and Changzhou A chain convenience store,experimental analysis was conducted,and dynamic response time intervals and dynamic customer numbers were analyzed.The research results indicate that using dynamic optimization strategies for route planning can more effectively reduce delivery costs and improve the utilization rate of delivery vehicles.Meanwhile,as the dynamic customer response time interval increases,the delivery cost also increases.Therefore,the distribution center should reasonably shorten the dynamic customer response time interval to timely meet customer needs,reduce delivery costs,and improve the freshness of goods.