用答案集编程联合优化空域流量与容量，提升调度效率。
Joint Air Traffic Flow and Capacity Management via Answer Set Programming
- 用答案集编程建模空域流量与容量的联合优化问题。
- 相比混合整数规划模型，该方法在求解性能上表现更优。
- 适合研究空管调度优化的学者与工程人员参考。
运行中的空中交通流与容量管理（ATFCM）通过平衡飞行需求与可用扇区容量，确保运行安全与高效。数学模型将需求-容量平衡问题建模为优化问题，以最大化效率并满足安全约束。然而，当前最先进研究通常单独优化飞机航迹（称为ATFM）或扇区配置（称为DAC），未探索联合优化是否带来收益。本文通过答案集编程（ASP）对联合ATFCM模型进行编码，部分填补了这一空白。在基于历史OpenSky Network飞行数据生成的实例上评估，结果表明：相比最先进的混合整数规划（MIP）模型，ASP模型在求解性能上更优；在小规模实例上，其表现与基于CASA的启发式方法相当。此外，相较于重新路由和延迟，扇区配置（DAC）带来的求解性能提升最大；但若允许无限制的DAC或重新路由，则会导致搜索空间震荡。
原文摘要 · Abstract (English)
Operational Air Traffic Flow and Capacity Management (ATFCM) balances flight demand with available sector capacity, to ensure safe and efficient operations. Mathematical models enhance operational ATFCM performance by framing demand-capacity balancing as an optimization problem, maximizing efficiency while adhering to safety constraints. However, SOTA research optimizes the aircraft trajectories (called ATFM) or the sector configuration (called DAC) separately. This leaves a research gap of whether joint optimization of ATFM and DAC can bring benefits. We partially address this limitation by introducing a joint ATFCM model with an encoding in Answer Set Programming (ASP). The ASP implementation is evaluated against two baselines applied to our joint model: a SOTA Mixed Integer Programming (MIP) model and an iterative CASA-based heuristic. Computational experiments utilize an instance generator fitted to historical OpenSky Network flight data. Our results indicate that the ASP model outperforms the MIP model, while ASP remains competitive against heuristics on small instances. Furthermore, while DAC has the largest improvement on solving performance compared to rerouting and delaying, unrestricted variants of DAC or rerouting lead to search space thrashing.
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