Research On Logistics UAV Resource Scheduling Based On Intelligent Optimization Algorithm

With the rapid development of e-commerce,the huge number of orders has brought increasing pressure to the express industry.The contradiction between the lagging development of the express delivery industry and the increasing number of orders is rapidly intensifying.The Courier of express delivery increases the randomness of express delivery,and increases the management cost,loss rate and delay rate of packages.The express delivery industry urgently needs to change its way of operation by introducing more modern logistics distribution system.In recent years,Unmanned Aerial Vehicles(UAVs)have been widely used in environmental monitoring,disaster relief and relay communications.Among them,logistics distribution is a very important application of UAV.This thesis studies the scheduling problem of heterogeneous logistics UAV in urban environment.The general solution of logistics scheduling algorithm is given.The advantages and disadvantages of various scheduling schemes are analyzed.Aiming at the logistics UAV scheduling problem in urban environment,the following two aspects are the focus of this thesis.1.An automatic delivery system is proposed to replace the single Courier delivery.It can effectively integrate existing facilities.In this system,there exists a scheduling problem of multiple UAV flights.We try to optimize the goals of customer satisfaction and total completion time.The scheduling problem is transformed into a mixed integer linear programming problem and a multi-objective decision method is proposed.A special coding method for small scale problems is considered.The approximate optimal solution of this problem is obtained by using variable neighborhood search algorithm and compared with other similar algorithms.2.For the case of excessive number of packages,an algorithm is proposed to reduce the search space.A solution encoding/decoding method for this problem is designed.An algorithm framework based on genetic algorithm is proposed to solve this scheduling problem.A new method of initial population generation and genetic manipulation are proposed.For performance evaluation and statistical analysis,this algorithm is compared with two existing algorithms.Experimental results show that this algorithm can effectively solve this problem.