A timely provision of emergency supplies is necessary in the case of a sudden power outage to minimize the loss of lives and property caused by the outage and to restore the living and working environment. Hou et al. [2] proposed an electrical emergency rescue transport model based on node-integrated weights for resource satisfaction, creating an objective function with the least amount of material supply and time leanness. A multi-point fault repair optimization model was created by Gao et al. [14], considering the distribution of first aid materials, the cooperation of repair teams, and the order of fixing broken equipment.

However, traditional electrical material rescue is primarily found in urban locations, making it more practical in favorable traffic and road conditions. The drawbacks of the conventional method are more severe in areas with poor road conditions, making drone transportation a more effective means to increase transportation effectiveness. Below are articles related to drone transport.

By modeling the power consumption of a drone as a nonlinear function of payload and trip time in a multi-traveling Drone routing problem model, considering a time window, Cheng et al. [15] further extended Dorling’s study. Their model offers logical and sub-gradient cuts to handle nonlinear power functions, using the branch-and-cut method to resolve the drone routing issue. In Gentili et al.’s [6] optimization issue, the value of emergency medical supplies is minimized depending on their perishability. Considering the drone’s battery life while transporting medical supplies, they hypothesize that each platform would only have one drone that could service one node at a time. The application of VRPD for heterogeneous drones is also investigated in several of the studies. The application of VRPD for heterogeneous drones is also investigated in several of the studies. For a heterogeneous fixed fleet drone routing problem, Chowdhury et al. [16] suggested a mixed integer linear programming model to resolve the cost of post-disaster inspections because of several aspects. Chen et al. [17] dealt with the drone fight trajectory planning problem for drones with different capabilities in a multi-area system. They initially created an algorithm to group regions into clusters, drawing inspiration from density-based clustering techniques. They then obtained approximations of the ideal point-to-point pathways for drones to carry out coverage tasks. In a different work by Chen et al. [18], they concentrated on the issue of coverage fight trajectory planning for heterogeneous drones. They suggested a method based on the Ant Colony System (ACS) to obtain enough drone pathways and cover every region thoroughly and effectively. According to the literature, the most significant barrier to drone distribution research is the constraint, which is one of the reasons why conventional vehicle route problem models are inapplicable to drone logistics.

Most previous studies have examined the issue from a drone-specific angle, ignoring the coordinated relationship between supply timeliness and drone scheduling, as well as target demand and the drones’ safety considerations. The construction of a drone scheduling model focusing on delivering electrical materials is crucial because models for the emergency transport of power drones are still uncommon.

In drone task assignments, heuristic algorithms are more frequently utilized [19]. Based on this, Wu et al. [20] perform the secondary selection operation of the genetic algorithm (GA) to improve the population diversity, and the secondary selection operation adopts the improved simulated annealing algorithm (SA) to solve the collaborative multitasking allocation problem more effectively. Han et al. [21] suggested a fuzzy elite strategy genetic algorithm to handle complicated issues. To increase its capacity to escape local optimal traps and hasten convergence, Wang et al. [22] suggested combining Simulated Annealing (SA) and Large Neighborhood Search (LNS) algorithms. Liu et al. [23] also proposed a collaborative optimization method combining GA and clustering methods to satisfy the task assignment of forest fires by drones. An upgraded cellular automaton (CA) and an optimal spanning tree technique were utilized by Li et al. [24] to build the path network and find the best routes between various endpoints. Zhang et al. [25] dynamically divided the particle swarm based on the particle mass and changed the topology of the algorithm. In addition, dynamic problems in tasking problems are common. Additionally, the PSO algorithm has been employed in additional combinations as a heuristic method: in their studies, Geng et al. [26] proposed a quantified particle swarm optimization algorithm for the task allocation problem of UAV clusters;. Shao et al. [27] proposed a hybrid strategy based on discrete particle swarm optimization for the many-to-one task planning problem in the case of a constructed a quantized particle swarm optimization technique. The simulated annealing algorithm is enhanced by Chen et al. [28] using a Levi distribution strategy, and it works well for both dynamic and static task assignment models. Yang et al. [29] use a distributed rational clustering algorithm for UAV clusters based on sensor networks and mobile information to improve the completion rate of UAV task assignments, providing a new distributed algorithm for task assignments.