Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current
anatomy to account for inter-fraction variations before daily treatment delivery. As this …

In the context of the Amazon Last-Mile Routing Research Challenge, this paper presents a
machine-learning framework for optimizing last-mile delivery routes. Contrary to most routing …

We introduce the concept of decision‐focused surrogate modeling for solving
computationally challenging nonlinear optimization problems in real‐time settings. The …

Data-driven inverse optimization (IO) aims to estimate unknown parameters in an
optimization model from observed decisions. The IO problem is commonly formulated as a …

This paper examines how data-driven personalized decisions can be made while
preserving consumer privacy. Our setting is one in which the firm chooses a personalized …

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

Decision-making problems are commonly formulated as optimization problems, which are
then solved to make optimal decisions. In this work, we consider the inverse problem where …

This article introduces and formalizes a novel stochastic method that combines inverse
simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study …

Urban infrastructure is vital for sustainable cities. In recent years, municipal governments
have invested heavily in the expansion of bike lane networks to meet growing demand …