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Title:New approaches to airline recovery problems
Author(s):Lee, Jane
Director of Research:Marla, Lavanya
Doctoral Committee Chair(s):Ouyang, Yanfeng
Doctoral Committee Member(s):Langbort, Cedric; Jacquillat, Alexandre
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):airline disruption management
stochastic optimization, integrated aircraft and passenger recovery
impact of climate change
Abstract:Air traffic disruptions result in fight delays, cancellations, passenger misconnections, creating high costs to aviation stakeholders. This dissertation studies two directions in the area of airline disruption management – an area of significant focus in reducing airlines’ operating costs. These directions are: (i) a joint proactive and reactive approach to airline disruption management, and (ii) a dynamic aircraft and passenger recovery approach to evaluate the long-term effects of climate change on airline network recoverability. Our first direction proposes a joint proactive and reactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. Specifically, it forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruption sources. It formulates a dynamic stochastic integer programming framework to minimize network-wide expected disruption recovery costs. Specifically, our Stochastic Reactive and Proactive Disruption Management (SRPDM) model combines a stochastic queuing model of airport congestion, a fight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop an online solution procedure based on look-ahead approximation and sample average approximation, which enables the model's implementation in short computational times. Experimental results show that leveraging partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1-2%, as compared to a baseline myopic approach that uses realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, fight cancellations and aircraft swaps. Our next direction studies the impact of climate change-imposed constraints on the recoverability of airline networks. We first use models that capture the modified payload-range curves for different aircraft types under multiple climate change scenarios, and the associated (reduced) aircraft capacities. We next construct a modeling and algorithmic framework that allows for simultaneous and integrated aircraft and passenger recovery that explicitly capture the above-mentioned capacity changes in aircraft at different times of day. Our computational results using the climate model on a worst-case, medium-case, and mild-case climate change scenarios project that daily total airline recovery costs increase on average, by 25% to 55.9% on average ; and by 10.6% to 156% over individual disrupted days. Aircraft-related costs are driven by a huge increase in aircraft swaps and cancelations; and passenger-related costs are driven by increases in disrupted passengers who need to be rebooked on the same or a different airline. Our work motivates the critical need for airlines to systematically incorporate climate change as a factor in the design of aircraft as well as in the design and operations of airline networks.
Issue Date:2020-07-17
Rights Information:Copyright 2020 Jane Lee
Date Available in IDEALS:2020-10-07
Date Deposited:2020-08

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