面对未知约束，通过交互式学习优化卫星拍摄计划。
Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach
- 设计可交互的约束获取方法，边学边优化
- 50任务场景下误差率降至17.9%，查询次数减少8成
- 适合航天调度、智能规划等需动态调整的场景
地球观测卫星调度是经典的组合优化问题。现有方法通常假设约束模型已完全明确，但实际中分离间隔、电力与热限制等约束常隐含于工程设计或高保真仿真器中，缺乏显式数学表达。本文研究在未知约束下的调度：目标明确，可行性需通过二元查询器逐步学习。基于简化模型（仅含成对分离与全局容量约束），提出保守约束获取（CCA）方法，在实践中高效识别合理约束，避免过度收紧。嵌入「学习-优化」（Learn&Optimize）框架，实现优化与针对性查询交替进行。在最多50个任务的合成实例上，该方法优于无知识贪心基线，且查询次数远少于先获取后求解的两阶段方法（FAO）。当任务数n≤30时，平均差距从65%-68%降至17.7%-35.8%；当n=50时，相比CP-SAT参考解（120秒内最优可行解），L&O平均提升至17.9%（对比FAO的20.3%），仅需21.3次主查询，耗时约其五分之一。
原文摘要 · Abstract (English)
Earth Observation (EO) satellite scheduling (deciding which imaging tasks to perform and when) is a well-studied combinatorial optimization problem. Existing methods typically assume that the operational constraint model is fully specified in advance. In practice, however, constraints governing separation between observations, power budgets, and thermal limits are often embedded in engineering artefacts or high-fidelity simulators rather than in explicit mathematical models. We study EO scheduling under \emph{unknown constraints}: the objective is known, but feasibility must be learned interactively from a binary oracle. Working with a simplified model restricted to pairwise separation and global capacity constraints, we introduce Conservative Constraint Acquisition~(CCA), a domain-specific procedure designed to identify justified constraints efficiently in practice while limiting unnecessary tightening of the learned model. Embedded in the \textsc{Learn\&Optimize} framework, CCA supports an interactive search process that alternates optimization under a learned constraint model with targeted oracle queries. On synthetic instances with up to 50~tasks and dense constraint networks, L\&O improves over a no-knowledge greedy baseline and uses far fewer main oracle queries than a two-phase acquire-then-solve baseline (FAO). For $n\leq 30$, the average gap drops from 65--68\% (Priority Greedy) to 17.7--35.8\% using L\&O. At $n{=}50$, where the CP-SAT reference is the best feasible solution found in 120~s, L\&O improves on FAO on average (17.9\% vs.\ 20.3\%) while using 21.3 main queries instead of 100 and about $5\times$ less execution time.
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