Optimization And Software Implementation Of An Path Planning Algorithm In Express Delivery Services

Smart logistics plays an important role in smart cities.In order to reduce the consumption on time and distance of express delivery,and to improve the delivery efficiency,a two-level distribution path planning algorithm based on heuristic intelligent search algorithm is studied,and the development of the express route planning system based on the cloud platform is completed.Combined with the characteristics of domestic express distribution,the express distribution is divided into two levels:city level and urban level.On the city level,distribution points are small and scattered,the delivery is affected by the real-time traffic of the city and distributed by the lorry,so the total time consumption could become the path quality evaluation value and a simulated annealing algorithm based on greedy self-evolution is proposed.This city level algorithm uses the initial path produced by greedy algorithm as the evolutionary chromosome to achieve the operation of gene exchange and fragment inversion,the metropolis criterion is used to determine whether to accept the new solution,always record the current optimal chromosome,and after the iteration to a certain number of times,annealing and cooling again to evolve until the termperature drops to the preset temperature stop.The results show that the improved algorithm is better than the traditional simulated annealing algorithm in its stability and current optimal distance when twenty points are delivered in a single time.On the urban level,distribution points are numerous and dense,the delivery are distributed by electric tricycles.Because of the tricycle is flexible and lightweight,and less affected by urban traffic,the ride distance consumption could become the path quality evaluation value,and in order to get the path of short distance consumption,a selection genetic algorithm based on the Metropolis criterion is proposed.In the genetic algorithm,the crossover rate is removed,the Metropolis criterion is used to determine the individual entering the next generation population,and the probability of accepting the poor solution in each generation of crossover and variation is adjusted,so as to avoid the precocious population.The simulation results show that the selection genetic algorithm not only effective eliminates the problem of easy fall into the local optimal solution in the search,but also the final planning result of the selection genetic algorithm is 10%shorter than the traditional algorithm,and is 4%shorter than the genetic simulated annealing algorithm.The two improved algorithms are applied on the Express Path Planning Service Cloud Platform,and the city level distribution path planning function is verified by the distribution points from the city center of Xi'an Yuantong Express,which proves that the function i?effective.The urban level distribution path planning function is verified by 50 real locations in the area of the South Second Ring of Xi,an city,and the urban level distribution obtained a more ideal path.