[HTML][HTML] Optimization under uncertainty and risk: Quadratic and copositive approaches
IM Bomze, M Gabl - European Journal of Operational Research, 2023 - Elsevier
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 …
the uncertainty inherent in almost all real-world optimization problems. The core principle of …
[PDF][PDF] A robust optimization approach to deep learning
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 …
loss to provide security guarantees. Yet, these methods require computations at each …
Robust conic satisficing
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 …
probability distribution is unobservable to the decision maker. Historical data provides some …
Robust optimization approaches in inventory management: Part A—the survey
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 …
Optimization (RO) in inventory management, highlighting its role in addressing uncertainties …
Flexible Optimization for Cyber-Physical and Human Systems
A Simonetto - IEEE Control Systems Letters, 2024 - ieeexplore.ieee.org
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 …
to-optimal decisions for human users to pick from, instead of a single, hardly explainable …
A Deep Generative Learning Approach for Two-stage Adaptive Robust Optimization
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" …
uncertainty that aims to balance costs of" here-and-now" first-stage decisions with those of" …
Robust Discrete Choice Model for Travel Behavior Prediction With Data Uncertainties
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 …
prediction. However, in many scenarios, the collected data for DCMs are subject to …
Robust Upper Bounds for Adversarial Training
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 …
of the adversarial loss to provide security guarantees against adversarial attacks. Yet, these …
Developing a multi-objective model for a multi-level supply chain of blood products under uncertainty and the global pandemic: a hybrid robust optimization approach
AM Esfandabadi, D Shishebori, MB fakhrzad… - Discover Applied …, 2024 - Springer
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 …
its products as a vital commodity. It has had adversely affected on the activities of blood …