Research On Logistics Vehicle Routing Optimization With Flexible Charging Strategy Considering Carbon Emissions

Environmental protection and intelligentization have become inevitable trends in the future development of transportation.As a for-profit enterprise,logistics companies need to improve their service levels and reduce operating costs,as well as have certain social responsibility to reduce greenhouse gas emissions.In order to meet this challenge,the"National Comprehensive Three-dimensional Transportation Network Planning Outline"specifically emphasizes that reducing the carbon emissions of logistics trucks is a key task to achieve carbon neutrality goals.Although electric vehicles can effectively promote low-carbon and environmental protection in logistics distribution,the carbon emissions generated by the electricity consumption during transportation are often ignored in the production process.Therefore,logistics companies should take measures to reduce electricity consumption and carbon emissions while actively promoting electric vehicles to achieve sustainable development.To reduce the distribution costs and carbon emissions of logistics companies using electric vehicles for urban distribution,this thesis calculates the power consumption of electric logistics vehicles in transportation processes based on payload and speed,and converts it into carbon emissions produced during power generation.The optimization objective is to minimize the sum of carbon emission costs,fixed vehicle costs,and transportation costs,and establish a Flexible Recharged Strateges for Multi-Depot Electric Vehicle Routing Problem with Time Windows Considering Carbon Emissions（MDEVRPTWFR-CE）model.In order to solve the model,a two-stage solution method is devised.In the first stage,a K-means clustering algorithm is used to transform the multi-depot problem into multiple single-depot problems;in the second stage,an adaptive large neighbourhood search algorithm（ALNS）with eight damage operators（five customer-related and three charging station-related）and five repair operators（three customer-related and two charging station-related）is designed to solve the transformed single-depot problems separately according to the characteristics of the model in this thesis.To test the performance of the model and algorithm proposed in this paper,the arithmetic cases and examples are solved separately using heuristic algorithms and the model and algorithm are compared.The effectiveness of the MDEVRPTWFR-CE model and the algorithm designed in this paper was tested by using the Gurobi solver and the two-stage heuristic to solve the small-scale example experiments,which yielded the same results.The results of the large-scale case study and the algorithm comparison experiments further show that the algorithm in this paper has good performance in terms of solution speed,accuracy and stability.The proposed MDEVRPTWFR-CE model in the paper is effective and practical,as it leads to lower total costs and CO₂ emissions compared to a fully charged route optimization model and a model without considering carbon emissions.The results of the study can provide decision support for the route planning of electric logistics vehicles in logistics enterprises,and achieve the goal of emission reduction and cost reduction.