Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming

By Chao Ning, Fengqi You. Computers and Chemical Engineering, 2019

314 citations recorded by the source corpus.

Abstract This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learning-based data-driven multistage optimization with a learning-while-optimizing scheme are presented.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/1b3f606886c910557cdd3a8eb5fe9878f582a43e

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In Control and Systems Engineering

In Management Science and Operations Research

In Ocean Engineering

Citations in the corpus

A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020, background): “[15] summarized and classified the main contributions of”