Research On Community Intelligent Logistics And UAV Scheduling Based On Intelligent Optimization Algorithms

With the wide population and rapid development of electronic commerce,a growing number of people are planning to buy goods and services online.However,the hidden worries under the prosperity of e-commerce,for instance,the sheer order volume,are putting increasing pressure on the logistics industry.The further development of the entire logistic industry is facing an emormous challenge by a range of rising transport costs such as fuel and manpower.Meanwhile,a wide variety of unforeseeable situations are forseeable during package transportation.These unforeseeable situations,such as road congestion and nobody at home,may lead to the delay of package transport,which certainly greatly reduces efficiency of logistics.Hence,it is quite urgent to use advanced technology and modern equipment to promote logistics efficiency,lower delivery costs and meet these challenges.Logistics "last mile" as the last section of the logistics transportation chain,whether the package can be delivered to customers timely and efficiently is very important to the efficiency of the whole logistics distribution process and the satisfaction of users.This thesis focuses on the logistics "last mile" problem.On the basis of full study of the existing results,a set of intelligent logistics model suitable for community environment and UAV logistics scheduling scheme are proposed.The main contents of this thesis are as follows:(1)Aiming at the "last mile" problem of logistics,a set of intelligent logistics model suitable for community environment is proposed and established.In this model,the express cabinet is placed on the bottom floor of each residential building,and can be used all day,so as to solve the problem that customers can not sign the package in time.On this basis,considering the actual situation that the capacity of the express cabinet is limited and the packages in the cabinet are not taken away in time,it is unrealistic to place each package in the express cabinet nearest to the residential building where the customer is located.Therefore,this thesis tries to optimize the pick-up distance of customers,takes the express container capacity and available capacity as the constraints of the problem,and proposes and designs a package distribution scheme to solve the problem.(2)Aiming at the problem of logistics "last mile",a set of UAV task scheduling scheme is proposed and designed.Considering the particularity of the community environment,the terminal distribution mode using UAV has more advantages than the traditional way.On this basis,this thesis takes the longest time for UAV to complete the logistics transportation task as the optimization objective,and takes the range and load of UAV as the constraints of the problem,and proposes and designs a set of UAV task scheduling scheme to solve the problem.(3)In order to solve the above package allocation scheme and UAV task scheduling scheme,a simulated annealing genetic algorithm is proposed.In order to improve the optimization performance,a new initial population generation algorithm is designed to try to find a better final solution and improve the convergence speed of the algorithm.For performance evaluation and statistical analysis,the algorithm is compared with some current intelligent optimization algorithms.The experimental results show that the simulated annealing genetic algorithm has better optimization effect on the above scheme.