Operations research specialists at the OCP Group, the Mohammed VI Polytechnic University, and the Polytechnique Montreal operationalized a system optimizing ...

Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision...

Recommender systems provide personalized recommendations to their users for items and services. They do that using a model that is tailored to each user to i...

Perturbations are universal in supply chains, and their appearance is getting more frequent in the past few years. These perturbations affect industries and ...

This paper develops an efficient hybrid algorithm to solve the credit scoring problem. We use statistical mathematical programming to develop new classificat...

Recently there has been a surge of interest in operations research~(OR) and the machine learning~(ML) community in combining prediction algorithms and optimi...

We present a Julia framework dedicated to partially-separable problems whose element function are detected automatically. This framework takes advantage of ...

We explore the factor exposure heterogeneity in green and brown stocks using the peer-exposure ratio. By creating peer groups of S&P 500 index firms over 201...

Le Douzième atelier de résolution de problèmes industriels de Montréal, qui eut lieu du 22 au 26 août 2022, fut organisé conjointement par le Centre de reche...

Recommender systems provide recommendations to their users for items and services by creating a model tailored to each user to infer their preferences based ...

This paper presents a multiobjective, mixed-integer linear programming (MILP) model that integrates production scheduling, inventory management, and vessel a...

The parameter configuration problem consists of finding a parameter configuration that provides the most effective performance by a given algorithm. This pap...

La plupart des méthodes d'apprentissage par renforcement pour le contrôle adaptatif de la signalisation routière nécessitent une formation partant de zéro po...

Providing the right data to a machine learning model is an important step to insure the performance of the model. Non-compliant training data instances may l...

In this paper, we study a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Specifically, we addres...

We study how the financial literature has evolved in scale, research team composition, and article topicality across 32 finance-focused academic journals fro...

Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations ei...

Crew pairing problems (CPP) are regularly solved by airlines to produce crew schedules. The goal of CPPs is to find a set of pairings (sequence of flights a...

The multi-depot scheduling problem (MDVSP) is one of the most studied problem in public transport service planning. It consists of assigning buses to each ti...

We explore the realized alpha-performance heterogeneity in green and brown stocks' universes using the peer performance ratios of Ardia and Boudt(2018). Focu...