Research On Electric Vehicle Routing Problem With Time Windows And Uncertain Energy Consumption

Vehicle routing problem with time windows(VRPTW)is an important optimization problem in logistics distribution scenarios.And electric vehicles are widely used in distribution activities because of their environmental friendliness and low energy cost.Therefore,the electric vehicle routing problem with time windows(EVRPTW)has received widespread attention.In the literature,the nonlinear energy consumption model is often used to calculate vehicle energy consumption before completing electric vehicle path planning.However,energy consumption is a typical uncertainty due to weather conditions and driving behavior,which cannot be directly calculated,affecting the effectiveness of electric vehicle path planning schemes.In addition,due to the lack of energy consumption data,the probability distribution of energy consumption cannot be obtained.Therefore,the EVRPTW under uncertain energy consumption is studied from the perspective of fuzzy optimization and robust optimization respectively.The main research results are as follows:Considering the limitation that the probability distribution of energy consumption is difficult to obtain,the EVRPTW under uncertain energy consumption is modeled and solved from the perspective of fuzzy optimization.Firstly,the fuzzy chance constraint model for EVRPTW under uncertain energy consumption has been established by describing energy consumption as a fuzzy variable.Then,an improved adaptive large neighborhood search algorithm(IALNS)has been designed,and the solution of model has been evaluated by fuzzy simulation.Finally,we design computing experiments for the purpose of verifying the effectiveness of the model and algorithm.Considering that the limitation of membership function of fuzzy optimization depends on experience,the EVRPTW under uncertain energy consumption is further modeled and solved from the perspective of robust optimization.Firstly,the robust optimization model for EVRPTW based on uncertain sets has been established through the uncertainty analysis of energy consumption.Then,two methods based on the idea of reformulation and adversarial have been proposed.Among them,the reformulation can only solve small-scale cases,while the adaptive memory programming(AMP)based on adversarial can solve larger-scale cases.Finally,we design computing experiments to verify the effectiveness of the algorithm and model and make the robustness analysis for the model.In summary,the EVRPTW under uncertain energy consumption has been solved from the perspectives of fuzzy optimization and robust optimization,respectively,and the effectiveness of the model and algorithm have been verified.The research results can provide theoretical support for electric vehicle path planning.