Dec 31, 2013 · We propose a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests.

Computational evidence in portfolio management and queueing confirm that our data-driven sets significantly outperform traditional robust optimiza- tion ...

Feb 25, 2017 · We propose a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests.

The basic idea of our approach is to train a neural network which can distinguish a true scenario from a corrupted or unrealistic scenario with high accuracy.

Data-driven optimization uses observations of the random variables as direct inputs to the mathematical programming problems. The first part of the tutorial ...

In this paper, we study a novel approach for data-driven decision-making under uncertainty in the presence of contextual information.

Jul 11, 2021 · This paper proposes a systematic approach to develop data-driven polyhedral uncertainty sets that mitigate these drawbacks.

Jan 25, 2022 · A data-driven robust optimization method is proposed by leveraging the merits of machine learning for decision-making under uncertainty.

This paper reviews several papers on DDRO and their applications on inventory, scheduling, portfolio selection, industries, and transportation issues.

Oct 22, 2024 · The distinctive feature of the method is that it requires neither statistical information on design variables nor calculation of the performance ...