基于Transformer层次预测的多星应急观测任务规划方法

应急观测任务规划是一个强时效性的复杂组合优化问题，必须在规定的时限内完成相应的计算。采用机器学习的方法对规划问题进行初始规划方案预测，可以有效地简化计算复杂度。为此，提出一种基于Transformer层次预测的多星应急观测任务规划方法，将多星任务规划的求解过程分解为3个步骤：首先，利用基于Transformer的任务可调度性预测模型预测待规划任务是否执行，得到预执行任务集合；然后，基于Transformer的任务分配模型对预执行任务集合分配卫星，得到初始规划方案；最后，利用基于随机爬山的约束修正算法对初始规划方案进行优化调整，得到可行规划方案。为验证所提方法的有效性，通过大量仿真实验与CPLEX优化器、标准遗传算法、长短期记忆网络等方法模型进行比较，实验结果表明所提方法计算耗时短，规划收益高，适用于多星观测任务快速规划。

Multi-satellite scheduling approach for emergency scenarios based on hierarchical forecasting with Transformer network

Emergency observation mission scheduling is a complex problem of combinatorial optimization with strong timeliness as the scheduling algorithm must complete the computation within the required time limit. Using machine learning methods to provide high-quality initial solutions for scheduling algorithms can effectively simplify the calculation process. For this reason, this paper proposes a multi-satellite scheduling approach for emergency scenarios based on hierarchical forecasting with Transformer network, which decomposes the scheduling into three steps. Firstly, using the Transformer-based task schedulability prediction model to predict whether the observation task will be executed or not, so as to obtain a set of tasks to be executed. After that, using the Transformer-based task allocation model to allocate the satellite to the task set, so as to obtain the initial scheduling scheme. Finally, a constraint modification algorithm based on random hill climbing is used to optimize the initial scheme, so as to obtain feasible scheduling schemes. To verify the effectiveness of the proposed method, simulation experiments are conducted, and the results are compared with those by CPLEX Optimization, standard genetic algorithm, Long Short-Term Memory and other methods. The simulation results show that the method proposed consumes a short calculation time and has high benefits, and is thus suitable for multi-satellite scheduling of emergency observation missions.