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

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

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...

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...

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

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

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

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

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

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

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...

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. ...

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

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

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

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...