Accelerating column generation for aircraft scheduling using constraint propagation

Grönkvist, Mattias

doi:10.1016/j.cor.2005.01.017

Cited by 33 publications

(15 citation statements)

References 21 publications

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“…The CP pricing subproblem implements an ad hoc labeling that balances the generation of negative reduced cost columns with the generations of good columns, that is, tours with small distances. As in Grönkvist (2006), CP is also used as part of a primal heuristic in Phase III.…”

Section: Traveling Tournament Problemmentioning

confidence: 99%

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Constraint programming-based column generation

Gualandi

Malucelli

2009

4OR-Q J Oper Res

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This paper surveys recent applications and advances of the constraint programming-based column generation framework, where the master subproblem is solved by traditional OR techniques, while the pricing subproblem is solved by constraint programming (CP). This framework has been introduced to solve crew assignment problems, where complex regulations make the pricing subproblem demanding for traditional techniques, and then it has been applied to other contexts. The main benefits of using CP are the expressiveness of its modeling language and the flexibility of its solvers. Recently, the CP-based column generation framework has been applied to many other problems, ranging from classical combinatorial problems such as graph coloring and two dimensional bin packing, to application oriented problems, such as airline planning and resource allocation in wireless ad hoc networks.

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Section: Traveling Tournament Problemmentioning

confidence: 99%

“…The research group of the Carmen System (currently Jeppesen) has applied this idea in Grönkvist (2006) and Gabteni and Grönkvist (2006) to an airline planning problem. In airline planning, a challenging problem consists in assigning a specific aircraft, identified by its unique tail number, to a particular set of flights.…”

Section: Tail Assignment Problemmentioning

confidence: 99%

Constraint programming-based column generation

Gualandi

Malucelli

2009

4OR-Q J Oper Res

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“…3 is well suited to the Tail Assignment problem, but shows slow convergence if applied as is Grönkvist (2006). The constraint programming approach described in Sect.…”

Section: A Hybrid Solution Approachmentioning

confidence: 99%

“…In Grönkvist, Grönkvist (2004, 2006 we have shown how constraint programming can be used as a standalone solution method, and as a preprocessing method, for the Tail Assignment problem. The second contribution of this article is a complete solution approach, which uses a novel combination of constraint programming and column generation to address the diverse requirements of long term planning and operations management.…”

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confidence: 99%

Combining column generation and constraint programming to solve the tail assignment problem

Gabteni

Grönkvist

2008

Ann Oper Res

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Within the area of short term airline operational planning, Tail Assignment is the problem of assigning flight legs to individual identified aircraft while satisfying all operational constraints, and optimizing some objective function. In this article, we propose that Tail Assignment should be solved as part of both the short and the long term airline planning. We further present a hybrid column generation and constraint programming solution approach. This approach can be used to quickly produce solutions for operations management, and also to produce close-to-optimal solutions for long and mid term planning scenarios. We present computational results which illustrate the practical usefulness of the approach.

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“…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 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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Accelerating column generation for aircraft scheduling using constraint propagation

Cited by 33 publications

References 21 publications

Constraint programming-based column generation

Constraint programming-based column generation

Combining column generation and constraint programming to solve the tail assignment problem

The Recoverable Robust Tail Assignment Problem

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