With increasing computing power, artificial intelligence (AI) and machine learning (ML) have
prospered, which facilitate the analysis of large datasets, especially those found in critical …

While the traditional facility location problem considers exogenous demand, in some
applications, locations of facilities could affect the willingness of customers to use certain …

The efficacy of robust optimization spans a variety of settings with uncertainties bounded in
predetermined sets. In many applications, uncertainties are affected by decisions and …

We study the problem of eliciting the preferences of a decision-maker through a moderate
number of pairwise comparison queries to make them a high quality recommendation for a …

We introduce a new class of adaptive policies called periodic-affine policies, which allows a
decision maker to optimally manage and control large-scale newsvendor networks in the …

Robust optimization is a popular paradigm for modeling and solving two-and multi-stage
decision-making problems affected by uncertainty. In many real-world applications, the time …

Static robust optimization has played an important role in radiotherapy, where the decisions
aim to safeguard against all possible realizations of uncertainty. However, it may lead to …

Traditionally, optimization of radiation therapy (RT) treatment plans has been done before
the initiation of RT course, using population-wide estimates for patients' response to therapy …

Designing policies for a network of agents is typically done by formulating an optimization
problem where each agent has access to state measurements of all the other agents in the …

Purpose We propose a treatment planning framework that accounts for weekly lung tumor
shrinkage using cone beam computed tomography (CBCT) images with a deep learning …