Dynamic airline scheduling: An analysis of the potentials of refleeting and retiming

Warburg, Valdemar; Hansen, Troels Gotsæd; Larsen, Allan; Norman, Hans; Andersson, Erik

doi:10.1016/j.jairtraman.2008.03.004

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…According to results reported in [1,10], we set the dynamic airline scheduling parameters as follows: a one-time reoptimization point is set 21-days prior to departure; the number Table 3, we show the daily profits for the original schedule and the robust schedule in a week's operation. For each individual day in this week, we generate 20 instances of demand and report the average profit for the original schedule (Column 2), the average profit for the robust schedule (Column 3), the absolute change in profitability (Column 4), and the percentage change in profitability (Column 5).…”

Section: Dynamic Scheduling Resultsmentioning

confidence: 99%

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Robust airline schedule design in a dynamic scheduling environment

Jiang

Barnhart

2013

Computers & Operations Research

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In the past decade, major airlines in the U.S. have moved from banked hub-and-spoke operations to de-banked hub-and-spoke operations to lower operating costs. In [1], it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re-fleeting and re-timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de-banked hub-and-spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition-based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline, that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones.

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Section: Dynamic Scheduling Resultsmentioning

confidence: 99%

“…In flight leg re-timing, flight departure and arrival times of a flight leg are altered to create new connecting itineraries through the hub to serve markets with higher than expected demands. Later, [9,10] present further experiments with dynamic scheduling under different scenarios, using data from a major European airline.…”

Section: Introductionmentioning

confidence: 99%

Robust airline schedule design in a dynamic scheduling environment

Jiang

Barnhart

2013

Computers & Operations Research

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“…In the literature much attention has been focused on large commercial airline problems, (Eltoukhy et al , 2017a, b; Haouari et al , 2011; Khaled et al ., 2018; Kohl et al , 2007; Liang and Chaovalitwongse, 2012; Liang et al , 2015; Maher et al , 2018; Warburg et al , 2008). On the other hand, few researches concerning the business aviation operation have been found.…”

Section: Literature Reviewmentioning

confidence: 99%

Aircraft routing problem model for fractional fleets using fault prognostics

Barreto

Abrahão

Vianna

2021

JQME

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PurposeThe objective of this work is to provide a novel aircraft allocation model for fractional business aviation. This model may provide decision-makers with alternative routing solutions that take into consideration preventive maintenance and failure prognostics information. The expected results are more efficient routing solutions when compared to conventional planning models, to help decision-makers improve operations and maintenance planning.Design/methodology/approachThe model is a mixed integer linear problem formulation addressing and considering preventive maintenance and failure prognostics for optimal operations. Numerical experiments were performed using both field and synthetic data to validate the proposed method. All instances are solved using branch, price and cut algorithms from open-source software.FindingsThe results obtained in this study show that the use of failure prognostics information in aircraft routing can provide improvements in overall planning. By choosing slightly longer flight legs, the flight cost will increase, but putting an aircraft with a higher risk of failure on a leg inbound to a maintenance base can reduce maintenance and overall operating cost.Originality/valueThe model and method provide decision-makers with routing solutions that consider new aspects of planning, not used in previous works, such as failure. Most of the literature focuses on solving routing problems for large commercial airlines. Considering that, few solutions are found in literature for fractional business operators, which have their own operational particularities, such as a company managing a fleet of aircraft belonging to multiple shareowners. In such operation, clients may not always fly in the aircraft that they are shareowners, but an aircraft from the fractional fleet of the same category. Here, the company managing the aircraft guarantees that an aircraft will be ready to attend client demands in minimum time. One of the major differences from other models of operation is the dynamic nature of its flight demands, thus requiring flexible and agile planning limiting the available time to find a routing solution.

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“…A study of the potential for dynamic airline scheduling, both re-fleeting and retiming, was conducted by Warburg et al (2008), primarily based on Jiang (2006) with additional components. Jiang and Barnhart (2009) addresses the same topic.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Demand driven dispatch and revenue management in a competitive network environment

Fry

Belobaba²

2016

J Revenue Pricing Manag

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Demand Driven Dispatch (D 3 ) is the reassignment of aircraft to flights close to departure to improve operating profitability, primarily by utilizing improved knowledge of expected demand from the airline's revenue management (RM) system. Previous studies of D 3 have not incorporated competition and have typically ignored or significantly simplified RM. In this study, the implementation of D 3 is tested with complete representations of RM systems in a network environment with competition. Results are from the Passenger Origin Destination Simulator, where stochastic demand by market chooses between competing airlines with alternative schedules and fare products. Findings include important competitive feedback effects from D 3 and insights about relationships between D 3 and both RM and pricing. Our findings indicate that the benefits of D 3 can be estimated at operating profit gains of 0.04-2.03 per cent, revenue gains of 0.02-0.88 per cent, and changes in operating costs of −0.08 to 0.13 per cent. However, use of D 3 may harm competitor airlines more than it aids the implementer. D 3 swaps early in the booking process can lead to heavy dilution. Late swaps lead to smaller increases in loads but substantial increases in revenue. The relationship between revenue-maximization and cost-minimization in profit-maximizing D 3 is highly influenced by the timing of swaps and revenue estimation.

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Dynamic airline scheduling: An analysis of the potentials of refleeting and retiming

Cited by 17 publications

References 11 publications

Robust airline schedule design in a dynamic scheduling environment

Robust airline schedule design in a dynamic scheduling environment

Aircraft routing problem model for fractional fleets using fault prognostics

Demand driven dispatch and revenue management in a competitive network environment

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