Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty
<jats:p> Air traffic disruptions result in flight delays, cancellations, passenger misconnections, and ultimately high costs to aviation stakeholders. This paper proposes a jointly reactive and proactive approach to airline disruption management, which optimizes recovery decisions in response...
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| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/144174 |
| _version_ | 1811089803091902464 |
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| author | Lee, Jane Marla, Lavanya Jacquillat, Alexandre |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Lee, Jane Marla, Lavanya Jacquillat, Alexandre |
| author_sort | Lee, Jane |
| collection | MIT |
| description | <jats:p> Air traffic disruptions result in flight delays, cancellations, passenger misconnections, and ultimately high costs to aviation stakeholders. This paper proposes a jointly reactive and proactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. The approach forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruptions. 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 flight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop a 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 even partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1%–2%, as compared with a myopic baseline approach based on realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, flight cancellations, and aircraft swaps. </jats:p> |
| first_indexed | 2024-09-23T14:24:55Z |
| format | Article |
| id | mit-1721.1/144174 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:24:55Z |
| publishDate | 2022 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1441742023-07-28T17:42:57Z Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty Lee, Jane Marla, Lavanya Jacquillat, Alexandre Sloan School of Management <jats:p> Air traffic disruptions result in flight delays, cancellations, passenger misconnections, and ultimately high costs to aviation stakeholders. This paper proposes a jointly reactive and proactive approach to airline disruption management, which optimizes recovery decisions in response to realized disruptions and in anticipation of future disruptions. The approach forecasts future disruptions partially and probabilistically by estimating systemic delays at hub airports (and the uncertainty thereof) and ignoring other contingent disruptions. 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 flight planning tool from Boeing/Jeppesen and an integer programming model of airline disruption recovery. We develop a 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 even partial and probabilistic estimates of future disruptions can reduce expected recovery costs by 1%–2%, as compared with a myopic baseline approach based on realized disruptions alone. These benefits are mainly driven by the deliberate introduction of departure holds to reduce expected fuel costs, flight cancellations, and aircraft swaps. </jats:p> 2022-08-01T16:16:54Z 2022-08-01T16:16:54Z 2020-07 2022-08-01T16:10:46Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144174 Lee, Jane, Marla, Lavanya and Jacquillat, Alexandre. 2020. "Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty." Transportation Science, 54 (4). en 10.1287/trsc.2020.0983 Transportation Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) SSRN |
| spellingShingle | Lee, Jane Marla, Lavanya Jacquillat, Alexandre Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty |
| title | Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty |
| title_full | Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty |
| title_fullStr | Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty |
| title_full_unstemmed | Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty |
| title_short | Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty |
| title_sort | dynamic disruption management in airline networks under airport operating uncertainty |
| url | https://hdl.handle.net/1721.1/144174 |
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