ASPaeroFlow: Decomposition Heuristics for Joint Air Traffic Flow & Capacity Management

2026-08-10

AI Summary

This study addresses the limitations of existing air traffic flow and capacity management approaches, which treat flow scheduling and dynamic airspace configuration in isolation, thereby perpetuating a cyclic dependency between fixed demand and fixed capacity. To overcome this, the paper proposes a novel joint optimization framework that efficiently coordinates flow and capacity decisions at medium-to-large scales, breaking away from conventional sequential paradigms. The method integrates answer set programming (ASP) for locally exact solving with a heuristic strategy based on instance-space decomposition, substantially improving solution quality. Experimental results demonstrate that dynamic airspace configuration plays a dominant role in achieving optimal performance, and the proposed approach consistently outperforms state-of-the-art exact algorithms and operational baselines on industrial-scale instances, offering a favorable balance between computational efficiency and solution quality.

Abstract

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.

Problem

• Air Traffic Flow Management
• Dynamic Airspace Configuration
• Joint Optimization
• Circular Dependency
• Computational Tractability

Innovation

• Joint ATFCM
• Instance-space decomposition
• Answer Set Programming
• Dynamic Airspace Configuration
• Heuristic optimization