A Hybrid Column Generation and Constraint Programming Optimizer for the Tail Assignment Problem

Gabteni, Sami; Grönkvist, Mattias

doi:10.1007/11757375_9

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…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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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: Tail Assignment Problemmentioning

confidence: 99%

Constraint programming-based column generation

Gualandi

Malucelli

2009

4OR-Q J Oper Res

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“…Hence tail assignment essentially combines fleet assignment and aircraft routing as describe before. There are several practical, operational advantages inherent to this approach, for details we refer to Gabteni and Grönkvist [3].…”

Section: The Tail Assignment Problemmentioning

confidence: 99%

“…The CSP modeled above is essentially what Gabteni and Grönkvist [3] refer to as CSP-TAS relax -"relax" since it does not cover some of the more complicated maintenance constraints. Still this model is used in the final integrated solution presented in [3].…”

Section: Remarksmentioning

confidence: 99%

“…We pursue this randomized approach with the tail assignment problem, for which we have access to a number of real-world instances. We show how this helps in overcoming the performance bottlenecks mentioned in [3,7].…”

Section: Introductionmentioning

confidence: 97%

“…Among other things it has been deployed in planning for transportation: Grönkvist [7] uses constraint programming to solve the tail assignment problem, an airline planning variant. Gabteni and Grönkvist [3] combine this with techniques from integer programming, in order to obtain a complete solver and optimizer, which is currently in use at a number of medium-sized airlines.…”

Section: Introductionmentioning

confidence: 99%

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Randomization in Constraint Programming for Airline Planning

Otten

Grönkvist

Dubhashi

2006

Principles and Practice of Constraint Programming - CP 2006

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We extend the common depth-first backtrack search for constraint satisfaction problems with randomized variable and value selection. The resulting methods are applied to real-world instances of the tail assignment problem, a certain kind of airline planning problem. We analyze the performance impact of these extensions and, in order to exploit the improvements, add restarts to the search procedure. Finally computational results of the complete approach are discussed.

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