In 1960, Ailsa Land and Alison Doig published the first linear programming-based branch-and-bound algorithm for the solution of mixed integer linear progra...

The purpose of the present note is to bring clarifications to certain concepts and surrounding notation of Aravkin et al. (2022). All results therein contin...

We develop a worst-case evaluation complexity bound for trust-region methods in the presence of unbounded Hessian approximations. We use the algorithm of Ar...

The fine-tuning of Large Language Models (LLMs) has enabled them to recently achieve milestones in natural language processing applications. The emergenc...

In this paper, a mixed integer nonlinear model for the short-term hydropower optimization problem considering operational constraints such as demand and st...

Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential community microgrid (RCM)...

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...

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

This work introduces a novel multi-fidelity blackbox optimization algorithm designed to alleviate the resource-intensive task of evaluating infeasible points...

Randomized Quasi-Monte Carlo (RQMC) methods provide unbiased estimators whose variance often converges at a faster rate than standard Monte Carlo as a functi...

Monte Carlo (MC) is widely used for the simulation of discrete time Markov chains. We consider the case of a \(d\)-dimensional continuous state space and w...

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...

We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal lik...

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 ...

"L'eau c'est la vie" is a well known french expression for "Water is life", which reflects the fact that water is undoubtedly the most vital resource in the ...

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...

How can retailers incentivize customers to make healthier food choices? Price, convenience, and taste are known to be among the main drivers behind such choi...

The Network Design Problem with Vulnerability Constraints and Probabilistic Edge Reliability (NDPVC-PER) is an extension of the NDPVC obtained by additionall...

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...

We consider a structural model to design and evaluate the American call, conversion, and put options embedded in corporate bonds. We use dynamic programmin...

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...

Reinforcement learning (RL) for partially observable Markov decision processes (POMDPs) is a challenging problem because decisions need to be made based on t...

Classical mean field games (MFG) have been concerned with large games amongst symmetrically influential agents with asymptotically negligible weight. In th...

Two-stage stochastic programs are a class of stochastic problems where uncertainty is discretized into scenarios, making them amenable to solution approaches...

High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mai...

We study the integration of multi-period assignment, routing, and scheduling of care workers for home health care services. In such a context, it is importa...

This paper addresses risk averse constrained optimization problems where the objective and constraint functions can only be computed by a blackbox subject to...

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternativ...

Improving neural network optimizer convergence speed is a long-standing priority. Recently, there has been a focus on quasi-Newton optimization methods, whi...

This paper investigates a variant of the traveling salesman problem (TSP) with speed optimization for a plug-in hybrid electric vehicle (PHEV), simultaneousl...

We investigate counterparty credit risk and credit valuation adjustments in portfolios including derivatives with early-exercise opportunities, under a net...

We consider the problem of minimizing the linear cost of multistate homogeneous series-parallel system given the nonlinear reliability constraint on the syst...

The popularity of bike-sharing systems has constantly increased throughout the last years. Most of such success can be attributed to their multiple benefits,...

The emerging demand for electric bicycles in recent years has prompted several bike-sharing systems (BSS) around the world to adapt their service to a new wa...

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

This paper presents a study on the best possible use of optimization models for the short-term hydropower scheduling problem. Different deterministic and sto...

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

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...

Tactical wireless networks are used in cases where standard telecommunication networks are unavailable or unusable, e.g. disaster relief operations. We fully...

We introduce a variant of the proximal gradient method in which the quadratic term is diagonal but may be indefinite, and is safeguarded by a trust region. ...

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

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

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

Historically, the training of deep artificial neural networks has relied on parallel computing to achieve practical effectiveness. However, with the increas...

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

This paper presents <code>FluxNLPModels.jl</code> and <code>KnetNLPModels.jl</code>, new Julia packages enabling a neural network, modelled with either Flux....

To cover the import taxes, a manufacturer typically charges a higher price in a foreign market than in its domestic market. The price difference can lead to ...

In this paper, we study the pickup and delivery problem with time windows and multiple compartments (PDPTWMC). The PDPTWMC generalizes the pickup and delive...

We consider a firm offering an opaque good over one selling season, that is, a product whose full characteristics are only revealed after the consumer comp...

We propose new algorithms with provable performance for online binary optimization subject to general constraints and in dynamic settings. We consider the su...

This paper studies linear-quadratic Stackelberg games with a major player (leader) and \(N\) minor players (followers). To design decentralized strategies ...

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

The Quadratic Knapsack Problem (QKP) is a combinatorial optimization problem that has attracted much attention over the past four decades. In this problem, o...

This paper introduces the consistent vehicle routing problem with stochastic customers and demands. We consider driver consistency as customer-driver assignm...

We investigate a facility location problem with modular capacity under demand uncertainty arising at Hydro-Québec, the largest public utility in Canada. We p...

This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise...

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

In July 2022, I received the EURO Gold medal at the 32nd EURO Conference held in Espoo, Finland. On this occasion I was asked to deliver a 30-minute presenta...

Given a ground-set of elements and a family of subsets, the set covering problem consists in choosing a minimum number of elements such that each subset cont...

Robotic Process Automation has emerged in recent years as an important field by allowing faster and more secure processes through a reduction in the risks or...

Seaports are highly vulnerable to climate-change induced events, which makes it necessary for them to invest in climate change adaptation measures to ensure ...

Optimizing static risk-averse objectives in Markov decision processes is challenging because they do not readily admit dynamic programming decompositions. Pr...

Given a set \(\mathcal{N}\) of size \(n\), a non-negative, integer-valued distance matrix \(D\) of dimensions \(n\times n\), an integer `(p\in\mathb...

In this paper, a new model is proposed for the real-time diesel genset optimal dispatch and unit commitment in remote microgrids. The objective is to reduce ...

For planning the operation of power transmission systems, which transport the energy produced by generation plants to customers centers, it is essential to e...

Microgrids (MGs) are regarded as effective solutions to provide ramping support to the main grid during heavy-load periods. Nevertheless, the uncertain renew...

This paper presents \(\texttt{Krylov.jl}\), a Julia package that implements a collection of Krylov processes and methods for solving a variety of linear pr...

In this paper, we investigate the problem of system identification for autonomous Markov jump linear systems (MJS) with complete state observations. We prop...

This paper first presents a time-series impact analysis of charging electric vehicles (EVs) to loading levels of power network equipment considering stochast...

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...

In this paper, we evaluate the synergy between variable renewable energy (VRE), electric mobility, and Vehicle to Grid (V2G) deployment for a small community...

The estimation of the structural model poses a major challenge as its underlying asset (the firm's asset value) is not directly observable. We extend the m...

This paper presents a novel rapid estimation method (REM) to perform stochastic impact analysis of grid-edge technologies (GETs) to the power distribution ne...