提出新启发式算法，联合优化空中交通流与空域配置
ASPaeroFlow: Decomposition Heuristics for Joint Air Traffic Flow & Capacity Management
- 将空域配置与流量管理联合建模，用解析集编程求解局部最优
- 在中大规模实例上比精确方法快数倍，且优于传统分步方案
- 证明动态空域调整对性能提升比流量控制更重要
尽管数学模型是空中交通流量与容量管理（ATFCM）的重要决策支持工具，现有方法仍将流量管理（ATFM）与动态空域配置（DAC）分离，导致固定需求与固定容量假设之间的未解循环依赖。虽然联合优化可解决该问题，但搜索空间扩大使得精确模型对中大型实例计算不可行。为此，我们提出ASPaeroFlow：一种针对联合ATFCM的启发式算法，结合实例空间分解与基于解析集编程的局部精确求解。我们在小到行业规模的实例上测试ASPaeroFlow，与精确方法和替代方案对比。结果表明：（1）该启发式在计算效率上介于精确方法与操作基准之间；（2）联合优化可优于分步优化；（3）消融实验显示，空域配置对解质量的影响大于流量指标。
原文摘要 · Abstract (English)
While mathematical models act as vital decision support systems for operational Air Traffic Flow and Capacity Management (ATFCM), existing approaches isolate Air Traffic Flow Management (ATFM) from Dynamic Airspace Configuration (DAC). This separation introduces an unresolved circular dependency between fixed-demand and fixed-capacity assumptions. Although joint optimization resolves this gap, the enlarged search space renders exact models computationally intractable for medium- to large-scale instances. To bridge this gap, we propose ASPaeroFlow: a heuristic for the joint ATFCM; it combines instance-space decomposition heuristics with a local exact approach using Answer Set Programming. We benchmark ASPaeroFlow from small to industry-sized instances and compare it with exact and alternative approaches. The results indicate that (1) the heuristic provides a computational middle ground between exact methods and operational baselines; (2) simultaneous optimization can outperform sequential optimization on joint ATFCM; and (3) an ablation study indicates that DAC has a larger impact on solution quality than flow measures.
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