01698171

Component

Runway systems are among the most stringent bottlenecks at international hub airports, which have been identified as a major source of airport inefficiency and flight delays. This paper considers the multi-objective tradeoffs among different performance indicators in runway scheduling in the presence of uncertainty. A multi-objective formulation aims to minimize flight delays, maximize airport throughput (or equivalently minimize the scheduling makespan), and minimize flight emissions, subject to a variety of constraints such as minimum separations, time window, runway occupancy and flight turnaround. The proposed multi-objective evolutionary algorithm is adjustable with the flexibility of choosing uncertainty sets, and offers a set of Pareto solutions for different stakeholders, without using scalarization of different objectives adopted by most similar studies. A real-world case study is carried out for one of the world’s busiest airports, Shanghai Pudong, which features 2 runways, 2 operation types, 12 uncertainty conditions and 3 optimization objectives. The results show that the proposed optimization algorithm has a significant effect in the process of population evolution, with a 75% reduction in flight delays and a 19% reduction in aircraft emissions between the first and last evolution generations. Compared with the First-Come-First-Serve strategy as the baseline, the proposed strategy further reduces flight delays by 58% and aircraft emissions by 12%, while improving the performance of airport and flight operations.

This paper was sponsored by TRB committee AV060 Standing Committee on Airfield and Airspace Capacity and Delay. Alternate title: Multi-objective Optimization of Airport Runway Scheduling under Uncertainty Conditions.

19-01907

English

15p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Air transportation; Airport runways; Case studies; Genetic algorithms; Optimization; Scheduling; Uncertainty

Shanghai Pudong International Airport

Evolutionary algorithms

Shanghai (China)

Aviation; Operations and Traffic Management; Planning and Forecasting

Transportation Research Board Annual Meeting 2019 Paper #19-01907

TRIS, TRB, ATRI

Dec 7 2018 9:47AM