Emma Frejinger

Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we … (see more)propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse formulations. We focus on human choice settings that can be formulated as (integer) linear programs. In such settings, existing inverse optimization and choice modelling methods infer preferences from observed choices but typically produce point estimates or fail to capture contextual shifts, making them unsuitable for risk-averse decision-making. Using a bounded-variance score function gradient estimator, we train a predictive model mapping contextual features to a rich class of parameterizable distributions. This approach yields a maximum likelihood estimate. The model generates scenarios for unseen contexts in the subsequent optimization phase. In a synthetic ridesharing environment, our approach reduces average post-decision surprise by up to 114

2026-03-16

arXiv (preprint)

A Capacitated Collection-and-Delivery-Point Location Problem with Random Utility Maximizing Customers

David Pinzon Ulloa

Ammar Metnani

2025-12-31

Comput. Oper. Res. (published)

What makes a good public EV charging station? A revealed preference study

Steven Lamontagne

Margarida Carvalho

Ribal Atallah

To determine the optimal locations for electric vehicle charging stations, optimisation models need to predict which charging stations users… (see more) will select. We estimate discrete choice models to predict the usage of charging stations using only readily available information for charging network operators. Our parameter values are estimated from a unique, revealed preferences dataset of charging sessions in Montreal, Quebec. We find that user distance to stations, proximity to home areas, and the number of outlets at each station are significant factors for predicting station usage. Additionally, amenities near charging stations have a neutral effect overall, with some users demonstrating strong preference or aversion for these locations. High variability among the preferences of users highlight the importance of models which incorporate panel effects. Moreover, integrating mixed logit models within the optimization of charging station network design yields high-quality solutions, even when evaluated under other model specifications.

2025-11-30

Transportation Research Part D: Transport and Environment (published)

The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem

Julie Kienzle

Serge Bisaillon

T. Crainic

Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses t… (see more)actical planning of intermodal railroad operations by introducing a new problem that simultaneously considers three consolidation processes and the management of a heterogeneous railcar fleet. We model the problem with a scheduled service network design with resource management (SSND-RM) formulation, expressed as an integer linear program. While such formulations are challenging to solve at scale, we demonstrate that our problem can be tackled with a general-purpose solver when provided with high-quality warm-start solutions. To this end, we design a construction heuristic inspired by a relax-and-fix procedure. We evaluate the methodology on realistic, large-scale instances from our industrial partner, the Canadian National Railway Company: a North American Class I railroad. The computational experiments show that the proposed approach efficiently solves practically relevant instances, and that solutions to the SSND-RM formulation yield substantially more accurate capacity estimations compared to those obtained from simpler baseline models. Managerial insights from our study highlight that ignoring railcar fleet management or container loading constraints can lead to a severe underestimation of required capacity, which may result in costly operational inefficiencies. Furthermore, our results show that the use of multi-platform railcars improves overall capacity utilization and benefits the network, even if they can locally lead to less efficient loading as measured by terminal-level slot utilization performance indicators.

2025-10-22

ArXiv (preprint)

Relative Explanations for Contextual Problems with Endogenous Uncertainty: An Application to Competitive Facility Location

Jasone Ram'irez-Ayerbe

2025-06-22

ArXiv (preprint)

Perspectives on optimizing transport systems with supply-dependent demand

Mike Hewitt

2025-03-25

INFOR. Information systems and operational research (published)

Supervised Large Neighbourhood Search for MIPs

Charly Robinson La Rocca

Jean-François Cordeau

Large Neighbourhood Search (LNS) is a powerful heuristic framework for solving Mixed-Integer Programming (MIP) problems. However, designing … (see more)effective variable selection strategies in LNS remains challenging, especially for diverse sets of problems. In this paper, we propose an approach that integrates Machine Learning (ML) within the destroy operator of LNS for MIPs with a focus on minimal offline training. We implement a modular LNS matheuristic as a test bench to compare different LNS heuristics, including our ML-enhanced LNS. Experimental results on the MIPLIB 2017 dataset demonstrate that the matheuristic can significantly improve the performance of state-of-the-art solvers like Gurobi and SCIP. We conduct analyses on noisy oracles to explore the impact of prediction accuracy on solution quality. Additionally, we develop techniques to enhance the ML model through loss adjustments and sampling routines. Our findings suggest that while random LNS remains competitive, our Supervised LNS (SLNS) outperforms other baselines and helps set the foundation for future research on ML for LNS methods that are both efficient and general.

2025-01-17

ArXiv (preprint)

An identification of models to help in the design of national strategies and policies to reduce greenhouse gas emissions.

Danielle Maia de Souza

Radhwane Boukelouha

Catherine Morency

Normand Mousseau

Martin Trépanier

2024-12-31

Transportation Research Procedia (published)

A Survey of Contextual Optimization Methods for Decision-Making under Uncertainty

Utsav Sadana

Abhilash Chenreddy

Erick Delage

Alexandre Forel

Thibaut Vidal

2024-12-31

European Journal of Operational Research (published)

Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem

Charly Robinson La Rocca

Jean-François Cordeau

The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of a… (see more)pplications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.

2024-11-30

Transportation Research Part E: Logistics and Transportation Review (published)

A model-free approach for solving choice-based competitive facility location problems using simulation and submodularity

Robin Legault

This paper considers facility location problems in which a firm entering a market seeks to open facilities on a subset of candidate location… (see more)s so as to maximize its expected market share, assuming that customers choose the available alternative that maximizes a random utility function. We introduce a deterministic equivalent reformulation of this stochastic problem as a maximum covering location problem with an exponential number of demand points, each of which is covered by a different set of candidate locations. Estimating the prevalence of these preference profiles through simulation generalizes a sample average approximation method from the literature and results in a maximum covering location problem of manageable size. To solve it, we develop a partial Benders reformulation in which the contribution to the objective of the least influential preference profiles is aggregated and bounded by submodular cuts. This set of profiles is selected by a knee detection method that seeks to identify the best tradeoff between the fraction of the demand that is retained in the master problem and the size of the model. We develop a theoretical analysis of our approach and show that the solution quality it provides for the original stochastic problem, its computational performance, and the automatic profile-retention strategy it exploits are directly connected to the entropy of the preference profiles in the population. Computational experiments indicate that our approach dominates the classical sample average approximation method on large instances, can outperform the best heuristic method from the literature under the multinomial logit model, and achieves state-of-the-art results under the mixed multinomial logit model. We characterize a broader class of problems, which includes assortment optimization, to which the solving methodology and the analyses developed in this paper can be extended.

2024-07-14

INFORMS Journal on Computing (published)

A logistics provider’s profit maximization facility location problem with random utility maximizing followers

David Pinzon Ulloa

Bernard Gendron

2024-06-30

Computers & Operations Research (published)