Path Planning And Solution Algorithm Of Pure Electric Vehicle Logistics Distribution With Time Window

Vehicle path planning is of great significance in industrial production to improve material operation efficiency and production efficiency.The rise and application of pure electric vehicles are conducive to further achieving the effect of energy conservation and emission reduction.Compared with traditional fuel vehicles,pure electric vehicles have the problems of limited range and insufficient power.In the process of transportation and distribution of electric vehicles,the location of charging piles should be considered to provide guarantee for vehicle range.In addition,the additional charging time and cost of electric logistics vehicles,as well as changes in the external traffic environment,will adversely affect the efficiency of logistics transportation.Combined with the time-varying characteristics of road traffic congestion,this paper establishes a pure electric vehicle logistics distribution path planning model with time window and simultaneous pickup and delivery,with the highest average customer satisfaction and the lowest logistics distribution cost as the optimization objectives.Firstly,a decision model based on distribution cost and power consumption function is established.In this model,distribution cost includes transportation cost,vehicle use cost,penalty cost for not arriving on time,and charging cost.The power consumption function is the energy loss caused by air resistance,tire rolling friction and transmission system.Secondly,a multi-objective genetic algorithm with elitist retention strategy(NSGA-II)is designed to optimize the path planning model of electric logistics vehicles,and an improved method is proposed to address the shortcomings of the traditional NSGA-II.A hybrid population is constructed using greedy algorithm and random rules,which ensures the population quality while maintaining population diversity,The adaptive genetic crossover mutation strategy is introduced in the crossover and mutation stages of the multiobjective genetic algorithm,and three neighborhood operators with different structures are designed in the individual mutation stage,which are selected and executed by the roulette wheel operator to enhance the local disturbance mechanism of the algorithm.Finally,through several simulations of the example,it is proved that the improved multi-objective genetic algorithm(H-NSGA-II)can effectively reduce the logistics distribution cost on the basis of maintaining the level of customer satisfaction unchanged.It can ensure the timeliness of picking up and delivering goods by logistics vehicles and improve the logistics transportation efficiency.The sensitivity analysis of the traffic congestion coefficient further verifies the effectiveness and stability of the model and algorithm proposed in this paper.