Bus scheduling in public transit consists in determining a set of bus schedules to cover a set of timetabled trips at minimum cost. This planning process has...

Determining optimal inventory replenishment decisions requires balancing the costs of excess inventory with shortage risks. While demand uncertainty has been...

The Harwell Subroutine Library (HSL) is a renowned suite of efficient and robust numerical algorithms designed to tackle complex mathematical problems such a...

We study the relation between the promotion of a cryptocurrency on Twitter and its return dynamics around pump-and-dump events. By analyzing abnormal retur...

This study addresses large-scale personnel scheduling problems in the service industry by combining mathematical programming with data mining techniques to...

We consider the set of graphs that can be constructed from a one-vertex graph by repeatedly adding a clique or a stable set linked to all or none of the vert...

We review the development of the concept of effective bandwidth from its origin in the planning and management of ATM networks. We start with the extension...

A growing body of recent literature analyzes the reaction of Robinhood (RH) investors to price movements at the daily frequency. As these investors tend to b...

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...