TY - JOUR

T1 - A multi-fidelity modelling approach for airline disruption management using simulation

AU - Rhodes-Leader, Luke

AU - Nelson, Barry

AU - Onggo, Stephan

AU - Worthington, David

PY - 2022/10/31

Y1 - 2022/10/31

N2 - Disruption is a serious and common problem for the airline industry. High utilisation of aircraft and airport resources mean that disruptive events can have large knock-on effects for the rest of the schedule. The airline must rearrange their schedule to reduce the impact. The focus in this paper is on the Aircraft Recovery Problem. The complexity and uncertainty involved in the industry makes this a difficult problem to solve. Many deterministic modelling approaches have been proposed, but these struggle to handle the inherent variability in the problem. This paper proposes a multi-fidelity modelling framework, enabling uncertain elements of the environment to be included within the decision making process. We combine a deterministic integer program to find initial solutions and a novel simulation optimisation procedure to improve these solutions. This allows the solutions to be evaluated whilst accounting for the uncertainty of the problem. The empirical evaluation suggests that the combination consistently finds good rescheduling options.

AB - Disruption is a serious and common problem for the airline industry. High utilisation of aircraft and airport resources mean that disruptive events can have large knock-on effects for the rest of the schedule. The airline must rearrange their schedule to reduce the impact. The focus in this paper is on the Aircraft Recovery Problem. The complexity and uncertainty involved in the industry makes this a difficult problem to solve. Many deterministic modelling approaches have been proposed, but these struggle to handle the inherent variability in the problem. This paper proposes a multi-fidelity modelling framework, enabling uncertain elements of the environment to be included within the decision making process. We combine a deterministic integer program to find initial solutions and a novel simulation optimisation procedure to improve these solutions. This allows the solutions to be evaluated whilst accounting for the uncertainty of the problem. The empirical evaluation suggests that the combination consistently finds good rescheduling options.

KW - Simulation

KW - Optimisation

KW - Integer Programming

KW - Multi-objective

KW - Transport

U2 - 10.1080/01605682.2021.1971574

DO - 10.1080/01605682.2021.1971574

M3 - Journal article

VL - 73

SP - 2228

EP - 2241

JO - Journal of the Operational Research Society

JF - Journal of the Operational Research Society

SN - 0160-5682

IS - 10

ER -