Toward Sustainable Airport Surface Operations: A Multi-Objective Collaborative Scheduling Method for Runway-Taxiway Systems Balancing Punctuality, Efficiency, and Carbon Footprint Control

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel multi-objective optimization framework for airport surface operations with demonstrated sustainability gains, useful for further research in air-ground collaboration.

🏢実務担当者:Offers a practical tool for airport operators and airlines to reduce fuel costs and emissions while improving punctuality, directly supporting sustainability reporting.

🏛政策担当者:Highlights the potential of operational optimization to achieve aviation emission reduction targets, informing policy on airport efficiency and green aviation.

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO2 emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs).

• openalex https://doi.org/10.3390/su18136837first seen 2026-07-08 05:22:40