Margarida Carvalho

nagisa.sugishita@mila.quebec

Postdoctorate - HEC Montréal

Publications

The Orders Beyond MLOR (OBM) clause: Enhancing flexibility in retailer–supplier agreements for perishable products

Maria João Santos

Pedro Amorim

Sara Martins

When shopping for perishable products, consumers typically prefer the freshest items, especially those with a short shelf life. With that in… (see more) mind, retailers establish strict contractual agreements with suppliers to ensure the fulfilment of their orders for perishable products. One key condition in these agreements is the Minimum Life On Receipt (MLOR) rule, which defines the maximum product age that the retailer will accept at full price. In this study, we propose a model that facilitates the negotiation of retailer-supplier terms to increase flexibility. Specifically, we define the share of orders retailers should accept beyond the MLOR at a discounted price. We formulate the problem as a bilevel program considering the individual objectives of the retailer (leader) and the supplier (follower), while also accounting for consumer demand driven by both price and product freshness. To address the bilevel problem, we employ a reformulation-and-decomposition algorithm adapted from the literature. We then compare the supply chain benefits of solving the bilevel program with those of optimising the retailer’s and supplier’s objectives jointly in a centralised approach, as well as to standard contract terms in which products are returned if the supplier does not meet the MLOR requirement. Our results demonstrate that flexible agreements offer significant benefits, with average profit increases of up to 4% for retailers and up to 13% for suppliers. Finally, we provide suggestions for designing new clauses that account for consumer demand variability and retailer’s order frequency.

2026-06-29

International Journal of Production Economics (published)

Intermediate Bilevel Optimization: Modeling Endogenous Follower Tie-Breaking Behavior

Maria Bazotte

Thibaut Vidal

In bilevel optimization, optimistic and pessimistic follower behaviors are the most commonly used forms to define how the follower ties-brea… (see more)ks among multiple optimal solutions. In this work, we go beyond these extreme tie-breaking behaviors and investigate the intermediate bilevel optimization program (I-BO), where the follower's selected optimal response is a decision-dependent random event, with a probability measure influenced by the leader's decision. We formally introduce a class of such endogenous measures, including the special case of strong-weak decision-dependent I-BO. We reformulate the I-BO as a Transformed I-BO (T-I-BO) with exogenous uncertainty by defining inverse and Markov-chain transformations, which represent the follower's response as a function of the leader's decision and exogenous randomness. We handle the T-I-BO's uncertainty via sample-average approximation (SAA), and we propose tailored approaches for its SAA program according to the chosen transformation. Computationally, our methods solve reasonable-sized instances efficiently and outperform the deterministic equivalent when available. Furthermore, experiments stress the critical need to accurately model follower tie-breaking behavior, particularly depending on its alignment with the leader's objective, as misspecification leads to suboptimal leader decisions.

2026-06-15

arXiv (preprint)

The strength of flow refueling location problem formulations and an extension to cyclic routing

Ribal Atallah

2026-06-10

4OR (published)

Decision Problems in Multilevel Linear Programming

We study the computational complexity of decision problems in …

2026-05-05

arXiv (preprint)

Unboundedness in Bilevel Optimization

Bárbara Rodrigues

Miguel Anjos

Abstract Bilevel optimization has garnered growing interest over the past decade. However, little attention has been paid to detecting and d… (see more)ealing with unboundedness in these problems, with most research assuming a bounded high-point relaxation. In this paper, we address unboundedness in bilevel and multilevel optimization by studying its computational complexity. We show that deciding whether an optimistic linear bilevel problem is unbounded is strongly NP-complete, even without coupling constraints. Furthermore, we extend the hardness result to the linear multilevel case, by showing that for each extra level added, the decision problem of checking unboundedness moves up a level in the polynomial hierarchy. Deciding unboundedness of a mixed-integer multilevel problem is shown to be one level higher in the polynomial complexity hierarchy than the decision problem for linear multilevel problem with the same number of levels. Finally, we introduce two algorithmic approaches to determine whether a linear bilevel problem is unbounded and, if so, return a certificate of unboundedness. This certificate consists of a direction of unboundedness and corresponding bilevel feasible point. We present a proof of concept of these algorithmic approaches on some relevant examples, and provide a brief computational comparison.

2026-03-25

Mathematical Programming (published)

Objective Misalignment in LLM-based Multi Agent Social Deception Game

Large language model–based multi-agent systems have attracted increasing attention for their strong performance in collaborative tasks and… (see more) social simulations. However, these interactive settings also introduce vulnerabilities, as a single agent's hidden goals and misaligned behavior can propagate misleading or malicious information throughout the system. In this work, we study these risks in the context of social deception games. We focus on the Werewolf Game, which requires agents to reason, communicate, and collaborate under asymmetric and incomplete information. We modify the individual objectives of some agents to induce benevolent, individualistic, and malevolent strategies that can make agents depart from the objectives of their own team. We evaluate how objective divergence affects game outcomes, collaboration, and goal satisfaction. Misaligned agents often succeed in achieving their own objectives, with effects amplified by role-based power asymmetries. Qualitative analyses further show that agents remain coherent and adaptive, strategically adjusting their reasoning, communication, voting behavior, and influence on group dynamics. These results indicate that risks in LLM-based multi-agent systems extend beyond collaborative task settings and persist even in environments where competition is structurally expected.

2026-02-28

AIWILD @ International Conference on Learning Representations (published)

openreview.net

Stackelberg Dynamic Location Planning under Cumulative Demand

Warley Almeida Silva

Sanjay Dominik Jena

Dynamic facility location problems predominantly suppose a monopoly over the service or product provided. Nonetheless, this premise can be a… (see more) severe oversimplification in the presence of market competitors, as customers may prefer facilities installed by one of them. The monopolistic assumption can particularly worsen planning performance when demand depends on prior location decisions of the market participants, namely, when unmet demand from one period carries over to the next. Such a demand behaviour creates an intrinsic relationship between customer demand and location decisions of all market participants, and requires the decision-maker to anticipate the competitor's response. This work studies a novel competitive facility location problem that combines cumulative demand and market competition to devise high-quality solutions. We propose bilevel mixed-integer programming formulations for two variants of our problem, prove that the optimistic variant is

2026-02-18

arXiv (preprint)

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

Steven Lamontagne

Emma Frejinger

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)

Understanding the role of depth in the neural tangent kernel for overparameterized neural networks

William St-Arnaud

Golnoosh Farnadi

2025-11-09

ArXiv (preprint)

Capacity Planning in Stable Matching

Federico Bobbio

Andrea Lodi

Ignacio Rios

Alfredo Torrico

We introduce the problem of jointly increasing school capacities and finding a student-optimal assignment in the expanded market. Due to the… (see more) impossibility of efficiently solving the problem with classical methods, we generalize existent mathematical programming formulations of stability constraints to our setting, most of which result in integer quadratically-constrained programs. In addition, we propose a novel mixed-integer linear programming formulation that is exponentially large on the problem size. We show that its stability constraints can be separated by exploiting the objective function, leading to an effective cutting-plane algorithm. We conclude the theoretical analysis of the problem by discussing some mechanism properties. On the computational side, we evaluate the performance of our approaches in a detailed study, and we find that our cutting-plane method outperforms our generalization of existing mixed-integer approaches. We also propose two heuristics that are effective for large instances of the problem. Finally, we use the Chilean school choice system data to demonstrate the impact of capacity planning under stability conditions. Our results show that each additional seat can benefit multiple students and that we can effectively target the assignment of previously unassigned students or improve the assignment of several students through improvement chains. These insights empower the decision-maker in tuning the matching algorithm to provide a fair application-oriented solution.

2025-10-02

Operations Research (published)

Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets

2025-09-21

WiML @ Neural Information Processing Systems (published)