Robust optimization and stochastic optimization are the two main paradigms for dealing with
the uncertainty inherent in almost all real-world optimization problems. The core principle of …

We propose robust optimization models and their tractable approximations that cater for
ambiguity-averse decision makers whose underlying risk preferences are consistent with …

Many state-of-the-art adversarial training methods leverage upper bounds of the adversarial
loss to provide security guarantees. Yet, these methods require computations at each …

In practical optimization, we typically model uncertainty as a random variable though its true
probability distribution is unobservable to the decision maker. Historical data provides some …

This work, the first part (Part A) of a comprehensive study, presents a survey on Robust
Optimization (RO) in inventory management, highlighting its role in addressing uncertainties …

We study how to construct optimization problems whose outcome are sets of feasible, close-
to-optimal decisions for human users to pick from, instead of a single, hardly explainable …

Two-stage adaptive robust optimization is a powerful approach for planning under
uncertainty that aims to balance costs of" here-and-now" first-stage decisions with those of" …

Discrete choice models (DCMs) are the canonical methods for travel behavior modeling and
prediction. However, in many scenarios, the collected data for DCMs are subject to …

Many state-of-the-art adversarial training methods for deep learning leverage upper bounds
of the adversarial loss to provide security guarantees against adversarial attacks. Yet, these …

The global COVID-19 pandemic has caused a substantial decrease in the blood supply and
its products as a vital commodity. It has had adversely affected on the activities of blood …