arXiv subjects

François Soumis

Publications and source records attributed to François Soumis.

4 recordsLinked to original sources

arXiv2025

Accelerated windowing for the crew rostering problem with machine learning

The crew rostering problem (CRP) for pilots is a complex crew scheduling task assigning pairings, or sequences of flights starting and ending at the same airport, to pilots to create a monthly schedule. In this paper, we propose an innovative solution method for the CRP that uses a windowing approach. First, using a combination of machine learning (ML) and combinatorial optimisation (CO), we quickly generate an initial solution. The solution is obtained with a sequential assignment procedure (\textit{seqAsg}) based on a neural network trained by an evolutionary algorithm. Then, this initial solution is reoptimized using a branch-and-price algorithm that relies on a windowing scheme to quickly obtain a CRP solution. This windowing method consists of decomposing the optimization horizon into several overlapping windows, and then optimizing each one sequentially. Although windowing has been successfully used in other airline applications, it had never been implemented for the CRP, due to its large number of horizontal constraints involving the whole planning horizon. We test our approach on two large real-world instances, and show that our method is over ten times faster than the state-of-the-art branch-and-price CRP solver GENCOL while providing solutions on average less than 1% away from optimality. We show that our windowing approach greatly benefits from being initialized with good-quality ML-based solutions. This is because the initial solution provides reliable information on the following windows, allowing the solver to better optimize the current one. For this reason, this approach outperforms other naive heuristics, including stand-alone ML or windowing.

math.OC↗

arXiv2021

Structured Convolutional Kernel Networks for Airline Crew Scheduling

Motivated by the needs from an airline crew scheduling application, we introduce structured convolutional kernel networks (Struct-CKN), which combine CKNs from Mairal et al. (2014) in a structured prediction framework that supports constraints on the outputs. CKNs are a particular kind of convolutional neural networks that approximate a kernel feature map on training data, thus combining properties of deep learning with the non-parametric flexibility of kernel methods. Extending CKNs to structured outputs allows us to obtain useful initial solutions on a flight-connection dataset that can be further refined by an airline crew scheduling solver. More specifically, we use a flight-based network modeled as a general conditional random field capable of incorporating local constraints in the learning process. Our experiments demonstrate that this approach yields significant improvements for the large-scale crew pairing problem (50,000 flights per month) over standard approaches, reducing the solution cost by 17% (a gain of millions of dollars) and the cost of global constraints by 97%.

cs.LG↗

arXiv2021

Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer

We present a case study of using machine learning classification algorithms to initialize a large-scale commercial solver (GENCOL) based on column generation in the context of the airline crew pairing problem, where small savings of as little as 1% translate to increasing annual revenue by dozens of millions of dollars in a large airline. Under the imitation learning framework, we focus on the problem of predicting the next connecting flight of a crew, framed as a multiclass classification problem trained from historical data, and design an adapted neural network approach that achieves high accuracy (99.7% overall or 82.5% on harder instances). We demonstrate the usefulness of our approach by using simple heuristics to combine the flight-connection predictions to form initial crew-pairing clusters that can be fed in the GENCOL solver, yielding a 10x speed improvement and up to 0.2% cost saving.

cs.LG↗

arXiv2020

Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype Baseline solver of Desaulniers et al. (2020) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%.

cs.AI↗