Micro-nano Earth Observation Satellite (MEOS) constellation has the advantages of low construction cost, short revisit cycle, and high functional density, which is considered a promising solution for serving rapidly growing observation demands. The observation Scheduling Problem in the MEOS constellation (MEOSSP) is a challenging issue due to the large number of satellites and tasks, as well as complex observation constraints. To address the large-scale and complicated MEOSSP, we develop a Two-Stage Scheduling Algorithm based on the Pointer Network with Attention mechanism (TSSA-PNA). In TSSA-PNA, the MEOS observation scheduling is decomposed into a task allocation stage and a single-MEOS scheduling stage. In the task allocation stage, an adaptive task allocation algorithm with four problem-specific allocation operators is proposed to reallocate the unscheduled tasks to new MEOSs. Regarding the single-MEOS scheduling stage, we design a pointer network based on the encoder-decoder architecture to learn the optimal single-MEOS scheduling solution and introduce the attention mechanism into the encoder to improve the learning efficiency. The Pointer Network with Attention mechanism (PNA) can generate the single-MEOS scheduling solution quickly in an end-to-end manner. These two decomposed stages are performed iteratively to search for the solution with high profit. A greedy local search algorithm is developed to improve the profits further. The performance of the PNA and TSSA-PNA on single-MEOS and multi-MEOS scheduling problems are evaluated in the experiments. The experimental results demonstrate that PNA can obtain the approximate solution for the single-MEOS scheduling problem in a short time. Besides, the TSSA-PNA can achieve higher observation profits than the existing scheduling algorithms within the acceptable computational time for the large-scale MEOS scheduling problem.

Agile Earth Observation Satellites (AEOSs) (abbreviated as agile satellite) with flexible attitude maneuvering capability greatly improve the Earth observation capability. However, the rapid increase in the number and length of visible time windows brings great challenges to agile satellite observation scheduling, and the specific time-dependent transition time of the agile satellites further complicates the observation scheduling problem. Therefore, the agile satellites observation scheduling problem has received extensive attention. To address the problem of observation scheduling of agile satellites, a mixed-integer nonlinear programming mathematical model aiming at maximizing observation profits is established with a consideration of the time-dependent transition time. Then, based on the framework of Evolutionary Squeaky Wheel Optimization (ESWO) algorithm with large neighborhood-oriented search capability, a heuristic agile satellites task-scheduling algorithm (ESASS) is proposed. In the proposed algorithm, five core operators of the ESWO algorithm are designed: analyzer, selection operation, mutation operation, priority sorter and constructor, according to the characteristics of AEOS observation scheduling problem. Then, an adaptive update strategy is designed based on the idea of simulated annealing algorithm to improve the solution speed and performance. A comparison of the proposed algorithm with the Adaptive Large Neighborhood Search (ALNS) algorithm, the classic heuristic insertion algorithm and the Genetic Algorithm (GA) -based observation scheduling algorithm verifies the effectiveness of the proposed algorithm. The experimental results show that the proposed ESASS algorithm can achieve greater benefits and number of observation tasks with shorter CPU runtime, and is applicable to the AEOSs observation scheduling problem with time-dependent transition time characteristics.