Optimization of Airport Surface Operations Under Uncertainty

Abstract

With the rapidly increasing air traffic volume and airports becoming bottlenecks of the air transportation system, integrated aircraft routing and flight scheduling have the potential to improve the efficiency of airport surface operations. Past research has mainly been focusing on solving subsets of airport operations based on deterministic data. This research develops a fast-time decision support algorithm that solves the integrated aircraft routing and flight scheduling at an airport in the presence of uncertainty. The algorithm is based on a machine job-shop scheduling formulation to model the integrated airport surface operations. The problem is formulated as a multistage stochastic program, and sample average approximation problems are solved to compute solutions. A proof-of-concept study case that considers 13 aircraft is conducted on a model of the northern field of Los Angeles International Airport. To test scenarios representing realistic conditions, perturbed input schedules of 30 min are generated using errors drawn from arrival and departure delay distributions. Outcomes are compared with results obtained with a first-come, first-served approach. Results illustrate that improved runway sequencing can be computed by the stochastic optimization, offering a better use of airport resources with reduced taxi and gate waiting times.