article

Flight operations recovery: New approaches considering passenger recovery

Authors:

Stephane Bratu,

Cynthia BarnhartAuthors Info & Claims

Journal of Scheduling, Volume 9, Issue 3

Pages 279 - 298

https://doi.org/10.1007/s10951-006-6781-0

Published: 01 June 2006 Publication History

Abstract

The sources of disruption to airline schedules are many, including crew absences, mechanical failures, and bad weather. When these unexpected events occur, airlines recover by replanning their operations. In this paper, we present airline schedule recovery models and algorithms that simultaneously develop recovery plans for aircraft, crews, and passengers by determining which flight leg departures to postpone and which to cancel. The objective is to minimize jointly airline operating costs and estimated passenger delay and disruption costs. This objective works to balance these costs, potentially increasing customer retention and loyalty, and improving airline profitability.

Using an Airline Operations Control simulator that we have developed, we simulate several days of operations, using passenger and flight information from a major US airline. We demonstrate that our decision models can be applied in a real-time decision-making environment, and that decisions from our models can potentially reduce passenger arrival delays noticeably, without increasing operating costs.

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Cited By

Zang HZhu JZhu QGao Q(2024)A proactive aircraft recovery approach based on airport spatiotemporal network supply and demand coordinationComputers and Operations Research10.1016/j.cor.2024.106599165:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.cor.2024.106599
Cadarso LVaze V(2023)Passenger-Centric Integrated Airline Schedule and Aircraft RecoveryTransportation Science10.1287/trsc.2022.117457:3(813-837)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1287/trsc.2022.1174
Birolini SJacquillat ASchmedeman PRibeiro N(2023)Passenger-Centric Slot Allocation at Schedule-Coordinated AirportsTransportation Science10.1287/trsc.2022.116557:1(4-26)Online publication date: 1-Jan-2023
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Flight operations recovery: New approaches considering passenger recovery

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Published In

cover image Journal of Scheduling

Journal of Scheduling Volume 9, Issue 3

June 2006

109 pages

ISSN:1094-6136

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Copyright © Copyright © 2006 Springer Science + Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2006

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Cited By

Zang HZhu JZhu QGao Q(2024)A proactive aircraft recovery approach based on airport spatiotemporal network supply and demand coordinationComputers and Operations Research10.1016/j.cor.2024.106599165:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.cor.2024.106599
Cadarso LVaze V(2023)Passenger-Centric Integrated Airline Schedule and Aircraft RecoveryTransportation Science10.1287/trsc.2022.117457:3(813-837)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1287/trsc.2022.1174
Birolini SJacquillat ASchmedeman PRibeiro N(2023)Passenger-Centric Slot Allocation at Schedule-Coordinated AirportsTransportation Science10.1287/trsc.2022.116557:1(4-26)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1287/trsc.2022.1165
Jacquillat A(2022)Predictive and Prescriptive Analytics Toward Passenger-Centric Ground Delay ProgramsTransportation Science10.1287/trsc.2021.108156:2(265-298)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1287/trsc.2021.1081
Guimarães MSoares CVentura R(2022)Decision Support Models for Predicting and Explaining Airport Passenger Connectivity From DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314715523:9(16005-16015)Online publication date: 1-Sep-2022
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Sun FLiu HZhang Y(2022)Integrated Aircraft and Passenger Recovery With Enhancements in Modeling, Solution Algorithm, and IntermodalismIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.309032923:7(9046-9061)Online publication date: 1-Jul-2022
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Evler JLindner MFricke HSchultz M(2022)Integration of turnaround and aircraft recovery to mitigate delay propagation in airline networksComputers and Operations Research10.1016/j.cor.2021.105602138:COnline publication date: 3-Jan-2022
https://dl.acm.org/doi/10.1016/j.cor.2021.105602
Hu YZhang PFan BZhang SSong J(2022)Integrated recovery of aircraft and passengers with passengers’ willingness under various itinerary disruption situationsComputers and Industrial Engineering10.1016/j.cie.2021.107664161:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.cie.2021.107664
Lee JLee KMoon I(2022)A reinforcement learning approach for multi-fleet aircraft recovery under airline disruptionApplied Soft Computing10.1016/j.asoc.2022.109556129:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109556
Suriyanarayanan RMurugappan KVasan A(2021)Intelligent Dynamic Replanning for Reduced Airport Operations2021 IEEE International Intelligent Transportation Systems Conference (ITSC)10.1109/ITSC48978.2021.9564858(355-360)Online publication date: 19-Sep-2021
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