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

• URL: http://arxiv.org/abs/2608.09315v1
• Date: Mon, 10 Aug 2026 08:57:18 GMT
• Title: ASPaeroFlow: Decomposition Heuristics for Joint Air Traffic Flow & Capacity Management
• Abstract summary: We propose ASPaeroFlow: a for the joint Air Traffic Flow and Capacity Management (ATFCM)<n>It combines instance-space decompositions with a local exact approach using Answer Set Programming.<n>We benchmark ASPaeroFlow from small to industry-sized instances and compare it with exact and alternative approaches.
• Score: 60.85136195443409
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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