We study server scheduling in multiclass service systems under uncertainty in the customer
arrival volumes. Common practice in such systems is to first identify staffing levels and then …

With stochastic integer programming as the motivating application, we investigate techniques
to use integrality constraints to obtain improved cuts within a Benders decomposition …

Optimization problems with discrete decisions are nonconvex and thus lack strong duality,
which limits the usefulness of tools such as shadow prices and the KKT conditions. It was …

This paper proposes a two-tier last-mile delivery model that optimally selects mobile depot
locations in advance of full information about the availability of crowd-shippers and then …

Stochastic Programming is a powerful modeling framework for decision-making under
uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used …

The distributed operating room (OR) scheduling problem aims to find an assignment of
surgeries to ORs across collaborating hospitals that share their waiting lists and ORs. We …

Multi-stage stochastic linear programs (MSLPs) are notoriously hard to solve in general.
Linear decision rules (LDRs) yield an approximation of an MSLP by restricting the decisions at …

Digital Annealer (DA) is a computer architecture designed for tackling combinatorial optimization
problems formulated as quadratic unconstrained binary optimization (QUBO) models. …

Given a mixed-integer set defined by linear inequalities and integrality requirements on some
of the variables, we consider extended formulations of its continuous (LP) relaxation and …

Inverse optimization—determining parameters of an optimization problem that render a given
solution optimal—has received increasing attention in recent years. Although significant …