Title | ||
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An Improved Integral Column Generation Algorithm Using Machine Learning for Aircrew Pairing |
Abstract | ||
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The crew-pairing problem (CPP) is solved in the first step of the crew-scheduling process. It consists of creating a set of pairings (sequence of flights, connections, and rests forming one or multiple days of work for an anonymous crew member) that covers a given set of flights at minimum cost. Those pairings are assigned to crew members in a subsequent crew-rostering step. In this paper, we propose a new integral column-generation algorithm for the CPP, called improved integral column generation with prediction (I(2)CG(p)), which leaps from one integer solution to another until a near-optimal solution is found. Our algorithm improves on previous integral column-generation algorithms by introducing a set of reduced subproblems. Those subproblems only contain flight connections that have a high probability of being selected in a near-optimal solution and are, therefore, solved faster. We predict flight-connection probabilities using a deep neural network trained in a supervised framework. We test I(2)CG(p) on several real-life instances and show that it outperforms a state-of-the-art integral column-generation algorithm as well as a branch-and-price heuristic commonly used in commercial airline planning software, in terms of both solution costs and computing times. We highlight the contributions of the neural network to I(2)CG(p). |
Year | DOI | Venue |
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2021 | 10.1287/trsc.2021.1084 | TRANSPORTATION SCIENCE |
Keywords | DocType | Volume |
crew pairing, machine learning, integral column generation, deep neural network | Journal | 55 |
Issue | ISSN | Citations |
6 | 0041-1655 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Adil Tahir | 1 | 0 | 0.34 |
Frédéric Quesnel | 2 | 0 | 0.34 |
Guy Desaulniers | 3 | 874 | 62.90 |
Issmail El Hallaoui | 4 | 0 | 0.34 |
Yassine Yaakoubi | 5 | 0 | 0.68 |