Intelligent Logistics Scheduling Optimization Method Of UAV And Its Application

With the rapid development of e-commerce,more and more people like to buy goods online,which brings challenges to the logistics industry.As the most important link in the logistics and distribution process,"Urban Last Mile Logistics"(ULML)plays a crucial role in the efficiency of the whole distribution process.The traditional logistics distribution mode is influenced by urban traffic,vehicle type and labor force.In addition,in the process of delivering goods to customers,there will also be some unexpected situations,such as customers are not at home,or due to weather changes,road congestion caused by the delivery delay,which will reduce the efficiency of logistics.Therefore,improving the efficiency of ULML becomes the key to solve the problem of logistics distribution.In the current research results,the combination of UAV and logistics is relatively few,but the research on Vehicle Routing Problem(VRP)is relatively more.In view of the problem of logistics in ULML,combining with the existing research results,this thesis puts forward a feasible solution to reduce the distribution time as the optimization goal,which has a practical effect on improving the efficiency of the logistics system.The main research contents of this thesis are as follows:1)Aiming at the problem of ULML,a new express delivery system is proposed.On this basis,the optimization condition of the single-target problem is determined by taking the average mission time of UAV as the constraint,and the load capacity and range limit of UAV as the constraint.2)In this thesis,according to the UAV distribution model,the representation and storage structure are designed to be used as the entrance to the solution of the evaluation function of the algorithm.In solving the scheduling problem,the advantages and disadvantages of the first simulated annealing process and the second simulated annealing process are analyzed,and an Improved Simulated Annealing algorithm(ISA)is proposed to solve the single-target UAV scheduling problem.The experimental comparison shows that the improved simulated annealing algorithm has better optimization effect than the general simulated annealing algorithm and is more effective for solving such problems.3)An adaptive tabu search algorithm is proposed to solve the logistics distribution model of ULML.In order to improve the optimization performance,the algorithm designs several dynamic tabu tables and divides the neighborhood space into several subsets.By comparing with Simulated Annealing algorithm and Local Search algorithm,it is proved that the Adaptive Tabu Search algorithm has better optimization effect on the problem.