## 2 Related Works and Contributions  
The traditional logistics scheduling problem is a special Vehicle Routing Problem (VRP). A great number of studies have focused on the field of VRP. [PERSON] and [PERSON] [4] first introduced the truck dispatching problem and proposed a procedure to search for the near-optimal solution. [PERSON] et al. [5] considered the VRP with simultaneous pickup-delivery and time windows, and a parallel Simulated Annealing algorithm was proposed for the problem. [PERSON] et al. [6] present a multiobjective and multidepot VRP. They developed a two-stage multiobjective evolutionary algorithm to deal with this problem. [PERSON] et al. [7] constructed the multiobjective VRP mathematical model of military logistics in wartime and improved the NSGA II algorithm by the introduction of Greedy algorithm, in order to solve the proposed multiobjective VRP problem. [PERSON] et al. [8] proposed the improved Ant Colony Algorithm to solve the classical Vehicle Routing Problem, where experimental analysis showed that the proposed algorithm was better than the traditional Ant Colony Algorithm in optimum value and the rate of convergence. [PERSON] et al. [9] proposed a variable neighborhood search algorithm to solve a kind of capacitated location-routing problem (CLRP). [PERSON] [10] put forward an improved ACO algorithm and built a path model to solve practical vehicle route problem in the emergency rescue event. [PERSON] [11] set up a vehicle routing problem (VRP) model with multiple fuzzy time windows, based on time-varying traffic flow. They also proposed an improved ITO algorithm to reduce the distribution cost and consumer dissatisfaction. As a special kind of VRP, UAV routing and scheduling problem has been applied in various scenarios. [PERSON] et al. [12] proposed two multitrip VRPs for UAV delivery that minimizes cost subject to a delivery time limit or minimizes the overall delivery time subject to a budget constraint. [PERSON] and [PERSON] [13] present a scenario in which UAVs were needed to provide continuous service and docking locations. They used a scheduling method to solve this problem. [PERSON] et al. [14] developed a nonlinear model to schedule UAV resources to the battlefield. [PERSON] et al. [15] proposed the network-centric multiagent system for real-time task scheduling of the UAV group, which achieved coordination and control of several UAVs in the group in order to perform joint tasks. [PERSON] et al. [16] proposed a mixed-integer programming model to determine the optimal collision-free schedule for multiple UAVs. The approach reduced the possibility of collision between UAVs by creating a gap between their arrival intervals to each node. [PERSON] et al. [17] proposed an efficient algorithm based on successive convex approximation and classical Dinkelbach method to reduce UAV flight energy consumption. [PERSON] et al. [18] suggested a trajectory planning method for two-UAV cooperative target locating. [PERSON] et al. [19] designed a generic scheduling framework of a fleet of micro UAVs. The framework employed multiple UAVs in sequential and parallel ways to perform a mission over a long period of time. A mixed integer linear programming (MILP) problem aiming at minimizing the total energy consumption was formulated after a series of linearization steps. The generic UAV scheduling framework can be applied in multiple domains comprising short and/or long-term UAV missions while ensuring uninterrupted service. Although UAVs can be used in many scenarios, there are few studies on the application of logistics UAV in urban environment. The scheduling method proposed in VRP related research cannot be directly applied to UAV logistics scheduling. The traditional VRP Problem includes customer set and vehicle set, and the main constraints include customer demand and actual road constraints, focusing on path planning. Whereas in UAVRP problems, UAV's operation track is relatively more flexible, smaller in size and greater in number.