Delay, Throughput and Emission Tradeoffs in Airport Runway Scheduling with Uncertainty Considerations

• Published in: Networks and spatial economics Vol. 21; no. 1; pp. 85 - 122
• Main Authors: Yin, Jianan, Ma, Yuanyuan, Hu, Yuxin, Han, Ke, Yin, Suwan, Xie, Hua
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2021; Springer Nature B.V
• Subjects: Adaptive algorithms; Air traffic management; Airports; Civil Engineering; Delay; Economic activity; Economics; Economics and Finance; Emission analysis; Emissions control; Evolutionary algorithms; Multiple objective analysis; Occupancy; Operations Research/Decision Theory; Pareto optimization; Pareto optimum; Regional/Spatial Science; Runways; Schedules; Scheduling; Tradeoffs; Uncertainty; Windows (intervals); Uncertainty; Multi-objective optimization; Runway scheduling; Evolutionary algorithm; Air traffic management; Performance tradeoff
• ISSN: 1566-113X; 1572-9427
• DOI: 10.1007/s11067-020-09508-3

Runway systems are among the most stringent bottlenecks at global hub airports, which have been identified as a major source of airport inefficiency. Runway system inefficiencies are manifested in multiple dimensions such as delay, throughput reduction and excessive emission, whose tradeoffs are investigated in this paper as part of an airport runway scheduling problem in the presence of uncertainty. We formulate a multi-objective optimization model aiming to minimize flight delays, maximize airport throughput, and minimize aircraft emissions, subject to a variety of constraints such as minimum separation, time window, runway occupancy and flight turnaround. The computational performance is enhanced with an efficient multi-objective evolutionary algorithm, with two mechanisms of adaptive and controllable time-coding and objective-guided individual selection. The proposed method is flexible in adjusting conservatism when it comes to optimization with uncertainty, and offers a set of Pareto optimal solutions for different stakeholders without using scalarization of different objectives. A real-world case study is carried out for one of the world’s buiest airports, Shanghai Pudong, under the case of 2 runways, 2 operation types, 12 uncertain conditions and 4 tradeoff scenarios. The computational results show that the proposed optimized method has overall advantages in improving the runway scheduling performance over some meta-heuristics and the First Come First Served strategy. The tradeoff analysis reveals that the minimum delay schedule is preferable for balancing delay, throughtput and emission. The findings provide managerial insights regarding traffic management measures for different stakeholders at high-density airports.