## 1 Introduction
[13] specified a delivery time range and imposed early and delayed delivery penalties on deliveries beyond that time range to establish a realistic coordination mechanism in the supply chain system. [PERSON] et al. [14] introduced the transportation mode conversion penalty cost in the objective function when constructing a multimodal transportation logistics model. Traditional algorithms commonly used to solve UAV task assignment and track planning problems include integer linear programming, the simulated annealing algorithm, etc., which can be solved by combining modern heuristic and swarm intelligence algorithms, such as a genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, and fish swarm algorithm. Among the traditional algorithms, a combination of local search and dynamic programming is used to solve the travel agent task assignment model with UAV participation [15]. Considering the multi-target problem, a linear weight method is proposed to transform it into a single-target UAV task planning problem, and then a hybrid algorithm combined with a real-coded fireworks algorithm are used to solve the multi-target UAV task assignment problem [16]. It has been proposed to use GridGSA for UAV track planning, apply the grid method to build an environment model, assign probability to corresponding grids, and then optimize the number and position of return points through a gravity search algorithm [17]. In modern algorithms, to solve the multi-target task assignment problem of a UAV, it is proposed that based on a rapid search by a quantum ant colony algorithm, the crossover and clone replication factors of the clonal selection algorithm are fused to improve the global search capability of the algorithm and avoid premature convergence in the search process [18]. A mixed population RRT UAV flight path planning method has been proposed to solve the flight path optimization problem. Based on a rapidly extended random tree (RRT) algorithm based on an environmental potential field, a self-optimizing population and co-optimizing population are introduced to improve the flight path segment so that the algorithm has local and global optimization capabilities [19]. There are algorithms based on the combination of a cooperative particle swarm algorithm, cooperative function, and suitable variables to discuss task allocation and track planning [20]. To avoid the \"dead-lock\" phenomenon caused by the task assignment of heterogeneous UAVs, an improved genetic algorithm based on multi-type gene coding was adopted for task assignment, and the Dubins path coordination method was adopted to generate the flyable track of the minimum turning radius of UAVs [21]. In summary, scholars' research on emergency logistics distribution is relatively complete, but it still needs to be improved in the following aspects:  
1. Based on research directions, existing research on drone delivery mainly focuses on the following three aspects: first, optimization model and algorithm research; the second is to explore more ways of operating drones; and the third is to study the impact of different parameters of a UAV on delivery [22]. However, in research on drone delivery services, recycling services are given little consideration [23]. 2. Most research on drone delivery uses non-real technical parameters. Only a few scholars use accurate technical parameters for research [24]. 3. Currently, to simplify the problem, most literature ignores some practical factors when constructing models or considers relatively few single factors. Only a few pieces of literature have proposed the expansion problem of combined distribution with multi-factor fusion [25]. 4. The standard FSTSP (Flying Sidekick Traveling Salesman Problem) model has no payload management constraints to control customers' orders on each drone route because without them, drone routes might not be feasible. 5.