Research On Time-varying Speed Vehicle Routing Optimization Based On Multi-objective Hybrid Particle Swarm Optimization

With the rapid development of the economy,the development of the logistics system has also increased rapidly.Among them,the role of transportation and distribution in the logistics link is crucial.The cost consumed in distribution accounts for more than 40% of the whole logistics operation cost.However,when calculating transport costs,most do not take into account the multi-point cost of drivers,which makes the calculated total distribution cost deviate from the actual situation.Due to the complexity of road conditions,vehicle speed changes at any time,affecting the entire distribution chain.This paper combines logistics-related elements to solve the multi-objective vehicle path problem with time windows in the case of time-varying speeds.This paper mainly completes the following work:(1)The speed distribution in different time periods is studied.The time of the day is divided into four stages,and the corresponding velocity distribution function of each stage is different: When driving in the unblocked time period,the vehicle speed can drive at a uniform speed as expected by the driver.The speed distribution function corresponding to the slow time period follows the lognormal distribution,the congestion time period follows the normal distribution,and the severe congestion time period follows the negative exponential distribution;When calculating the total cost of distribution,considering that the cost will change dynamically due to the different number of drivers serving customers,when establishing the multi-objective model,the multi-point cost of drivers is considered,and the time window constraint is considered.A multi-objective vehicle routing mathematical model with the minimum total cost of distribution,the maximum satisfaction of distribution customers and the shortest total distance of transportation route is constructed.(2)Due to the simple operation and high search efficiency of particle swarm optimization algorithm,this paper selects multi-objective particle swarm optimization algorithm(MOPSO)to solve the multi-objective optimization model,but there are still some defects.Therefore,the selection,crossover and mutation mechanism of genetic algorithm is introduced into MOPSO,and a multi-objective hybrid particle swarm optimization algorithm is designed to improve the search ability of the algorithm,The designed multi-objective hybrid particle swarm optimization algorithm is used to solve the above model.(3)In order to verify the above established models and algorithms,based on the actual urban distribution case provided by a logistics company,mark the location coordinates of the distribution center and customer points in QGIS software,set the cost,speed and other relevant parameter values,program and calculate with MATLAB software,and calculate the experimental results using MOPSO and multi-objective hybrid particle swarm optimization algorithm respectively.The results show that,compared with the calculation results of MOPSO,using multi-objective hybrid particle swarm optimization algorithm can reduce the average total distribution cost by 12.98%,improve the average customer satisfaction by 12%,and reduce the average vehicle driving distance by25.94%.It is proved that the multi-objective hybrid particle swarm optimization algorithm is reasonable and effective in solving vehicle routing optimization.The research on vehicle routing problem based on multi-objective hybrid particle swarm optimization algorithm under the condition of time-varying speed has certain theoretical significance for the future development of logistics industry.