Research On UAV Multi-objective Delivery Optimization Strategy In Urban Logistics Delivery Environment

At the present,technology of UAV logistics is still immature.Its load and endurance capability are relatively weak.Therefore,improving the efficiency of UAV delivery is crucial.This paper mainly researches the allocation scheme used multiple UAVs to complete the assignment.In this regard,this article mainly considers two common delivery modes under the urban logistics delivery environment,one is the home delivery,another is the point-to-point delivery model.This paper main tasks as follows:(1)This paper first proposes a multi-objective allocation optimization strategy for multi-UAV based on the number of parcels.First,the packages are grouped by the maximum and minimum distance algorithm,and the results of the algorithm are related to the selected scale factor.Therefore,when setting the coefficients,the dichotomy is used to iterate over the coefficients to obtain the optimal coefficients.Among them,the evaluation criterion of the optimal coefficient is that the number of packages that need to be adjusted is the smallest.Then,based on the UAV load capacity constraints and the UAV cruising capacity constraints,the grouping obtained by the maximum and minimum distance algorithm is adjusted to obtain a grouping plan that satisfies the constraints and the shortest path distribution order within the group.Finally,in order to avoid the situation of local optimal solution,a random factor is set,and each package randomly generates a random factor.Select some packages for packet exchange,and iteratively arrive at the delivery plan with the shortest delivery path.The method is verified by a randomly generated package data set,and the result proves that the method is superior in the distribution efficiency evaluation indicators such as the number of batches and the total distribution path length.(2)This paper proposes a UAV multi-objective delivery optimization strategy based on the delivery point as the delivery unit based on the first split and then combined distribution.First,pre-process the data at the delivery point.Assume that the total weight of the packages at the delivery point is different,and there are cases where the total weight is much greater than the load capacity of the UAV.Therefore,the parcels on the distribution point whose total weight at the distribution point whose total weight is much larger than the load capacity of the UAV is divided,and the distribution set data set that satisfies the constraints is obtained.Then,the improved ant colony algorithm is used to allocate the split distribution groups,and at the same time,the distribution order of the distribution points assigned to the same batch of the same UAV in the distribution plan is adjusted.Finally,a multi-target delivery solution for unmanned aerial vehicles based on split and combination delivery is formed.The method is verified by the randomly generated package data set and its corresponding collection point data set,and the result proves that the method had better delivery efficiency evaluation indexes such as expedited package delivery order,number of delivery batches and the total delivery path length.