Dynamic Airline Disruption Management Under Airport Operating Uncertainty

Marla, Lavanya; Jacquillat, Alex; Lee, Jane

doi:10.2139/ssrn.3082518

Cited by 9 publications

(7 citation statements)

References 55 publications

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“…Consequently, many research projects aim at integration and partial automation of the decision-making process in an AOCC [27], [28]. Thus, some scholars see the highest potential to recover delays in changing critical aircraft and crew assignments, which may even include the cancellation of flight cycles to mitigate schedule disruptions [3], [4], [29]- [33]. Some studies further consider dynamic cost indexing, i.e., speeding up the flight cruise segment to reduce arrival delays [33] and some compare it to retiming the departure of other aircraft to ensure passenger transfers [34]- [38].…”

Section: B Status Quo On Airline Schedule Recoverymentioning

confidence: 99%

“…Thus, some scholars see the highest potential to recover delays in changing critical aircraft and crew assignments, which may even include the cancellation of flight cycles to mitigate schedule disruptions [3], [4], [29]- [33]. Some studies further consider dynamic cost indexing, i.e., speeding up the flight cruise segment to reduce arrival delays [33] and some compare it to retiming the departure of other aircraft to ensure passenger transfers [34]- [38]. Other approaches have a local focus and consider recovery options mostly during the turnaround at a major airport, which includes the possibility to shorten or omit entire sub-processes, or assign extra resources to speed up standard operating procedures [5], [39]- [44].…”

Section: B Status Quo On Airline Schedule Recoverymentioning

confidence: 99%

“…The variety of approaches is also reflected by the incorporated objectives, which aim at minimising flight and/or passenger delays [42], [44]- [46], while others set out to optimise the associated cost of delay and recovery [5], [30], [32], [33], [35], [43], [47]. The latter objective includes the optimal assignment of recovery options to those flights with the highest priority (business utility), which is reflected by a progressive increase of costs at higher departure delays [7], [46].…”

Section: B Status Quo On Airline Schedule Recoverymentioning

confidence: 99%

See 2 more Smart Citations

Stochastic Delay Cost Functions to Estimate Delay Propagation Under Uncertainty

et al. 2022

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This project has received funding from the German Federal Ministry of Economic Affairs and Energy (BMWI) within the LUFO-V project OPsTIMAL under grant agreement No 20X1711M. Further funding was received from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 783287 (Engage KTN). The opinions expressed herein reflect the authors' views only. Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information contained herein.

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Section: B Status Quo On Airline Schedule Recoverymentioning

confidence: 99%

Section: B Status Quo On Airline Schedule Recoverymentioning

confidence: 99%

Section: B Status Quo On Airline Schedule Recoverymentioning

confidence: 99%

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Stochastic Delay Cost Functions to Estimate Delay Propagation Under Uncertainty

et al. 2022

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“…Admittedly, disruption costs for airlines are very important and considering that flight delays impact for 2007 in the USA reached the $30 billion, while almost a decade later, disruptions costs airlines and their customers the double amount of $60 billion (Jimenez-Serrano and Kazda, 2017), one may say that the more efficient disruption management an airlines does, the less distressed passengers it has to handle and fewer costs to afford. The delay impact, among others, includes lost revenue, passenger rights (compensation and re-accommodation) and crew overtime costs (Lee et al, 2017). According to Gershkoff (2016) disruption costs consist the 8% of airline revenues.…”

Section: Distressed Passengers and Customer Service Managementmentioning

confidence: 99%

Cooperation Agreements and Customer Portfolio Management in Hotel Industry: The Airline Market Segment

Sergopoulos¹,

Poulaki²

2019

ojtmr

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This paper aims to fill the gap in the literature regarding business-to-business (B2B) cooperation agreements between hotels and airlines, providing the readers with insights regarding the airline market segment in the hotels" customer portfolio management and their benefits for both parties. Literature review includes legislation issues, operations management and customer service management process, as a catalyst for these agreements. A hidden tourism business sector is also presented and refers to intermediaries that often handle such agreements. Hotels and airlines are tourism businesses and cooperating bodies aiming at high quality customer service and travel experience optimization. This dimension is not met in the relevant literature and therefore, this paper contributes to the theory with business insights of such B2B cooperation environment. Empirical methodology includes a primary survey with questionnaires distributed in the five-star hotel units of Athens, aiming to investigate their agreements with the airlines operating from Athens International Airport. The results highlight the background, the conditions and the benefits that both hotels and airlines gain from such business-to-business contracts and are used to evaluate airline market segment for the hotel industry, underlining its contribution in occupancy rates and revenues, as well as in seasonality mitigation, among others. beverage departments, mitigation of seasonality, etc. In the following sections the background of B2B environment between airlines and hotels is developed, the research question resulting from literature review gap, the research methodology and the primary survey details along with the findings and the conclusions. The way forward is suggested as well.

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“…decision variable), thereby enabling a parochial routine for estimating said features during ADM. Some of these models provide decent estimates of different flight schedule features for operational ADM during schedule execution [3][4][5]. However, their solution processes require considerable amount of time to evaluate the objectives and constraints posed by unscalable local and global routines that define the optimization of a monolithic system.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling for Airline Disruption Management

Ogunsina,

Okolo

2021

Preprint

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Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.

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Dynamic Airline Disruption Management Under Airport Operating Uncertainty

Cited by 9 publications

References 55 publications

Stochastic Delay Cost Functions to Estimate Delay Propagation Under Uncertainty

Stochastic Delay Cost Functions to Estimate Delay Propagation Under Uncertainty

Cooperation Agreements and Customer Portfolio Management in Hotel Industry: The Airline Market Segment

Artificial Neural Network Modeling for Airline Disruption Management

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