## 1 Introduction
divided downlink requests into ordinary and urgent requests, using fast planning strategy to deal with emergency request first, then use the remaining resources to process ordinary requests [13]. [PERSON] et al. proposed two types of solution construction graphs with the number of tasks completed as the main objective, which improved the performance of the ant colony optimization algorithm by limiting the search neighborhood [14]. [PERSON] et al. considered the inter-satellite bandwidth capacity and solved it using iterative tree search algorithm based on the integer programming model [15]. [PERSON] et al. proposed a planning method combining SVM and NSGA-II based on the idea of classification for satellite data transmission planning [16]. [PERSON] et al. proposed a conflict resolution technique for satellite downlink scheduling by constructing elite initial schedule [17]. [PERSON] et al. solved the satellite range scheduling problem with priority constraints by designing coding and selection methods in genetic algorithm [18]. [PERSON] et al. proposed the conflict degree of data transmission and solved it by combining tabu search and genetic algorithm [19]. [PERSON] et al. proposed a K-shortest path genetic algorithm for the balance between response time and resource utilization [20]. [PERSON] et al. optimized the ground-satellite link planning with respect to three aspects: link switching frequency, routing update frequency, and relay satellite configuration [21]. [PERSON] et al. designed a rote genetic algorithm operator to guide the algorithm for the problem of data transmission window collisions in the transmission of satellite cluster data transmission [22]. [PERSON] et al. established a global scheduling optimization model, including satellite ground transmission and ground transmission, including conflict resolution model in the process of satellite ground transmission and data transmission model in the process of ground transmission [23]. Integrated satellite scheduling is to schedule satellite observation and data transmission simultaneously. [PERSON] et al. established a mixed integer linear programming model for the integrated satellite scheduling problem using a directed acyclic graph for determining candidate solution options [24]. [PERSON] et al. described a two-step binary linear programming formula considering energy consumption, which solved the downlink scheduling sub-problem first and, then, the constellation task scheduling problem [25]. [PERSON] et al. considered that all observation data in the same area need to be downloaded to the same ground station, and proposed the construction heuristic algorithm and downlink scheduling algorithm [26]. [PERSON] et al. considered the impact of weather uncertainty on mission success to ensure the maximum reliability of mission scheduling, proposed two planning modes based on periodic triggering and event triggering, and adopted a two-stage scheduling scheme to optimize observation and downlink operation simultaneously [27]. [PERSON] et al. proposed a solution method based on quantum genetic algorithm considering the model of energy constraint and storage capacity constraint [28]. [PERSON] et al. modeled the integrated satellite scheduling problem as a mixed integer programming model and proposed the concept of conflict request set to improve the performance of genetic algorithm [29]. This article focuses on the low latency requirements of users in the multi-satellite and ground stations scheduling problem (MGSS). In this paper, the calculation process of the visible time window between the satellite and the ground station is firstly presented. Then, model the problem as a resource-constrained heterogeneous vehicle routing problem with time window (RC-HVRPTW) according to the characteristics of MGSS. The primary optimization objective is to minimize task delay in MGSS. A simulated annealing algorithm with a tabu list and start time decision (SATLD) is proposed to solve this problem.