Automatic document summarization aims at creating a shorter version of one or more documents to help users digest large amounts of information more easily by...

In this paper, we describe a branch-and-price algorithm for the personalized nurse scheduling problem. The variants that appear in the literature involve a ...

Benders decomposition has been applied significantly to tackle large-scale optimization problems with complicating variables, which, when temporarily fixed, ...

We introduce an iterative solver named MINARES for symmetric linear systems \(Ax \approx b\), where \(A\) is possibly singular. MINARES is based on t...

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...

The Twelfth Montreal IPSW took place on August 22-26, 2022, and was jointly organized by the Centre de recherches mathématiques (CRM) and the Institute for D...

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...

Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modi...

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...