Airline disruption management: A literature review and practical challenges

Hassan, Labeed; Santos, Bruno F.; Vink, Jeroen

doi:10.1016/j.cor.2020.105137

Cited by 43 publications

(27 citation statements)

References 65 publications

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“…Some papers do not consider a specific supply chain disruption (for example [ 62 – 65 ]. Other reviews can be linked to distribution or infrastructure disturbances (e.g., [ 36 , 40 , 66 ]. Two papers solely highlight supply disruptions in commercial supply chains [ 67 , 68 ]…”

Section: Research Methods and Study Designmentioning

confidence: 99%

Meta-analysis of Supply Chain Disruption Research

Novoszel

Wakolbinger

2022

Oper. Res. Forum

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The purpose of this chapter is to provide insights into literature on supply chain disruption research with a specific focus on future research opportunities. A structured meta-literature review approach covering 93 literature reviews was chosen. Quantitative and qualitative content analysis and bibliographic network analysis are applied to highlight trends and research gaps. The meta-analysis shows the current and past academic discourse on supply chain disruptions. Furthermore, this research establishes a research framework and highlights future research opportunities. The research points to research topics that should be addressed in the future. The paper provides a holistic understanding of literature on supply chain disruptions in the commercial and humanitarian context.

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Section: Research Methods and Study Designmentioning

confidence: 99%

Meta-analysis of Supply Chain Disruption Research

Novoszel

Wakolbinger

2022

Oper. Res. Forum

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“…Given a setting at major hub airports, where up to 100 aircraft of the same airline are turned around during so-called hub-banks within a time frame of about three hours, this can be a highly iterative and lengthy procedure and is unlikely to result in cost-minimal solutions. 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].…”

Section: B Status Quo On Airline Schedule Recoverymentioning

confidence: 99%

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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“…These stages are closely associated with each other, which indicates that disturbances in any of the stages often affects the other stages negatively. As noted in [19], disruptions are mainly categorized into flight delays, flight cancellation, fleet availability, and airport disruption. To minimize the overall probability of the aforementioned disruptions, several researchers have developed robust flight scheduling models integrating two or three of the flight scheduling stages.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of ATM research, including flight scheduling, there have been few studies that conducted multi-objective optimization with meta-heuristic approaches and analyzed the obtained optimal solutions in detail with the data-mining approaches [27] [28] [29], although significant design knowledge can be potentially extracted from those optimal or suboptimal solutions. Furthermore, optimal schedules obtained in the related works were not created in consideration of the weather conditions and air traffic flow although they have a large impact on flight delay, which is one of the four disruption types [19]. The other three disruptions, namely, flight cancellation, fleet availability, and airport disruption, occur as irregular events, complicating the mitigation of thereof probabilities, and are thereby often managed through recoverybased approaches [30] [31] [19].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, optimal schedules obtained in the related works were not created in consideration of the weather conditions and air traffic flow although they have a large impact on flight delay, which is one of the four disruption types [19]. The other three disruptions, namely, flight cancellation, fleet availability, and airport disruption, occur as irregular events, complicating the mitigation of thereof probabilities, and are thereby often managed through recoverybased approaches [30] [31] [19]. In contrast, flight delays, whose causes vary depending on the locations [32], could relatively be reduced using a proactive approach such as simulation-based approaches and delay analyses, including the aforementioned GHP studies.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Takeoff Time Optimization Using Cellular Automaton-Based Simulator

et al. 2021

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Although schedule design has further potential to reduce airline operation costs and flight delay, the effectiveness of the globally optimal schedule design integrating air traffic flow has not been discussed thus far. This paper presents a global multi-objective takeoff time optimization to design efficient flight schedules that lead to minimal congestion and provide sufficient resilience against traffic problems. NSGA-II is adopted as the multi-objective optimization technique in this study. The objective functions include minimization of the total arrival delay and total fuel consumption because these are key performance indicators of air traffic management (ATM). The design variable used in this study is the takeoff time offset of each flight landing at the Tokyo International Airport. 607 design variables were used in this study. The range of the design variables was ±300 s to investigate the effect of a minor variation in the takeoff time. A cellular automaton-based model was utilized to simulate the interaction of the flights with each other. The results of the simulations demonstrated that the obtained optimal solutions could drastically reduce the total arrival delay and total fuel consumption by 1500 min and 80 tons, respectively. The spacing adjustments of one of the optimum flight schedules, in comparison to the original flight schedule, were reduced by 80% in the en-route and terminal airspaces. Additional analyses suggest that it is preferable to have longer takeoff time intervals for flights originating from the same point during congestion hours than those during non-congestion hours. This indicates that the optimization of ground movements in airports improves the efficiency of air traffic operations. INDEX TERMSAir traffic control, air traffic management, cellular automaton, flight scheduling, ground holding program, NSGA-II, schedule design

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Airline disruption management: A literature review and practical challenges

Cited by 43 publications

References 65 publications

Meta-analysis of Supply Chain Disruption Research

Meta-analysis of Supply Chain Disruption Research

Stochastic Delay Cost Functions to Estimate Delay Propagation Under Uncertainty

Multi-Objective Takeoff Time Optimization Using Cellular Automaton-Based Simulator

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