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<p>We investigate how airspace constraints—such as airways, restricted areas, and flow management measures—affect aircraft fuel burn and airline economics when these constraints are explicitly embedded in an integrated aircraft–network optimization. We build a system-of-systems framework that couples aircraft sizing/performance with a linear programming network allocation. Realistic mission profiles are inferred from ADS-B tracks at ten major European airports using DBSCAN clustering and fuzzy logic to parameterize climb, cruise, and descent phases, capturing lateral distance extensions relative to great-circle baselines. Compared with simplified great-circle missions, the data-driven missions yield more conservative economics: daily profit and margins are lower once increased block times and fuel burn are accounted for. Across case studies, the optimal aircraft obtained with data-driven missions carries fewer seats and exhibits a slightly larger wing planform than the great-circle-optimal counterpart, indicating that realistic trajectories can shift the aircraft–network co-optimum. The methodology provides OEMs and airlines with design and planning solutions that better reflect operational realities, while also highlighting limitations in dataset representativeness and clustering uncertainty.</p>

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Integrated aircraft and network optimization with airspace constraints

  • 期刊论文

  • 发表日期：

    2025年10月30日

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  • Alejandro A. Rios Cruz,
  • Bento S. de Mattos,
  • José Alexandre T. G. Fregnani

摘要

We investigate how airspace constraints—such as airways, restricted areas, and flow management measures—affect aircraft fuel burn and airline economics when these constraints are explicitly embedded in an integrated aircraft–network optimization. We build a system-of-systems framework that couples aircraft sizing/performance with a linear programming network allocation. Realistic mission profiles are inferred from ADS-B tracks at ten major European airports using DBSCAN clustering and fuzzy logic to parameterize climb, cruise, and descent phases, capturing lateral distance extensions relative to great-circle baselines. Compared with simplified great-circle missions, the data-driven missions yield more conservative economics: daily profit and margins are lower once increased block times and fuel burn are accounted for. Across case studies, the optimal aircraft obtained with data-driven missions carries fewer seats and exhibits a slightly larger wing planform than the great-circle-optimal counterpart, indicating that realistic trajectories can shift the aircraft–network co-optimum. The methodology provides OEMs and airlines with design and planning solutions that better reflect operational realities, while also highlighting limitations in dataset representativeness and clustering uncertainty.