Tail Assignment in Practice

Grönkvist, Mattias; Kjerrström, Jens

doi:10.1007/3-540-27679-3_21

Cited by 16 publications

(34 citation statements)

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“…Recoverable robustness has been also addressed within this topic. Froyland, Mahen, and Wu (2013) formulate a recoverable robust aircraft tail assignment model; they solve the model using a two-phase algorithm within Benders decomposition and incorporating enhancement techniques; they compare their algorithm with the method used by Gronkvist (2005), demonstrating that they can reduce recovery costs. Eggenberg (2009) states that better schedules are less sensitive to perturbations and are easier to recover when severe disruptions occur; he presents a general model that encodes all feasible recovery schemes (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Combining robustness and recovery in rapid transit network design

Cadarso

Marı́n

2016

Transportmetrica A: Transport Science

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When designing a transport network, decisions are made according to an expected value for network state variables, such as infrastructure and vehicle conditions, which are uncertain at a planning horizon of up to decades. Because disruptions, such as infrastructure breakdowns, will arise and affect the network on the day of operations, actions must be taken from the network design. Robust network designs may be implemented but they are extremely expensive if disruptions do not realise. In this paper, we propose a novel approach to the network design problem where robustness and recovery are combined. We look for the trade-off between efficiency and robustness accounting for the possibility of recovering from disruptions: recoverable robust network design. Computational experiments drawn from fictitious and realistic networks show how the presented approach reduces the price of robustness and recovery costs as compared to traditional robust and non-robust rapid transit network design approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Combining robustness and recovery in rapid transit network design

Cadarso

Marı́n

2016

Transportmetrica A: Transport Science

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“…We develop a proxy robust algorithm using the connection cost of Grönkvist [15] in defining the cost of a flight string c (4) and (10) is solved to find an optimal tail assignment. Since the aircraft routing and, by extension, the tail assignment problem is a feasibility problem, the selection of a cost function is used only as a proxy to favour specific connection lengths and improve robustness.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…The connection cost function presented in the PhD thesis of Grönkvist [15] attempts to improve the robustness of tail assignments by assigning costs to flight connections based on their length. We illustrate the connection cost function implemented for our representative proxy robust model and in the first stage of our recoverable robust model in Figure 1.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…Because the Grönkvist function has appeared in the literature to solve the tail assignment problem [15,16] we choose to solve RRTAP relative to this function. Table 4: Analysis of connection cost functions.…”

Section: Comparison Of Recoverable Robust Solutions and Grönkvist Solmentioning

confidence: 99%

“…The RRTAP includes costs due to flight delays, cancellation, and passenger rerouting, and the recovery stage includes cancellation, delay, and swapping options. To highlight the benefits of simultaneously solving planning and recovery problems in the RRTAP we compare our tail assignment solution with the tail assignment generated using a connection cost function presented in Grönkvist [15]. Using airline data we demonstrate that by developing a better tail assignment plan via the RRTAP framework, one can reduce recovery costs in the event of a disruption.…”

Section: Introductionmentioning

confidence: 99%

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The Recoverable Robust Tail Assignment Problem

Froyland

Maher²,

2014

Transportation Science

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Schedule disruptions are commonplace in the airline industry with many flight-delaying events occurring each day. Recently there has been a focus on introducing robustness into airline planning stages to reduce the effect of these disruptions. We propose a recoverable robustness technique as an alternative to robust optimisation to reduce the effect of disruptions and the cost of recovery. We formulate the recoverable robust tail assignment problem (RRTAP) as a stochastic program, solved using column generation in the master and subproblems of the Benders decomposition. We implement a two-phase algorithm for the Benders decomposition incorporating the Magnanti-Wong [21] enhancement techniques. The RRTAP includes costs due to flight delays, cancellation, and passenger rerouting, and the recovery stage includes cancellation, delay, and swapping options. To highlight the benefits of simultaneously solving planning and recovery problems in the RRTAP we compare our tail assignment solution with the tail assignment generated using a connection cost function presented in Grönkvist [15]. Using airline data we demonstrate that by developing a better tail assignment plan via the RRTAP framework, one can reduce recovery costs in the event of a disruption.

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