发表机构
TU Wien; University of Potsdam; CNRS; Artois University; Frequentis AG（维也纳技术大学; 波茨坦大学; 法国国家科学研究中心; 阿图瓦大学; 弗昆蒂斯公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对空中交通流量与容量联合管理的循环依赖及中大规模实例计算不可行问题，提出ASPaeroFlow启发式算法，经基准测试验证其性能优于序列优化，且DAC对解质量影响更大。
AI 中文摘要
数学模型是空中交通流量与容量联合管理（ATFCM）的重要决策支持系统，但现有方法将空中交通流量管理（ATFM）与动态空域配置（DAC）分离开，这种分离在固定需求与固定容量假设间引入了未解决的循环依赖。尽管联合优化可解决该问题，但扩大的搜索空间使精确模型对中大规模实例而言计算上不可行。为弥合这一差距，本文提出ASPaeroFlow——一种用于联合ATFCM的启发式算法，它将实例空间分解启发式与基于回答集编程的局部精确方法相结合。我们从小型到行业规模的实例对ASPaeroFlow进行基准测试，并将其与精确方法及其他方法对比。结果表明：（1）该启发式在精确方法与运营基线间提供了计算上的中间方案；（2）联合ATFCM的同时优化性能可优于序列优化；（3）消融研究显示，DAC对解质量的影响大于流量措施。
英文摘要
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.
CommentsTo cite this contribution, please cite the ATMOS 2026 paper not the arXiv version; the arXiv version includes the supplementary material. Experimental results are available at: https://doi.org/10.5281/zenodo.21869481
DOI：10.4230/OASIcs.ATMOS.2026.13
