English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

This paper reviews recent advances in the field of optimization underuncertainty via a modern data lens, highlights key research challenges andpromise of data-driven optimization that organically integrates machinelearning and mathematical programming for decision-making under uncertainty,and identifies potential research opportunities. A brief review of classicalmathematical programming techniques for hedging against uncertainty is firstpresented, along with their wide spectrum of applications in Process SystemsEngineering. A comprehensive review and classification of the relevantpublications on data-driven distributionally robust optimization, data-drivenchance constrained program, data-driven robust optimization, and data-drivenscenario-based optimization is then presented. This paper also identifiesfertile avenues for future research that focuses on a closed-loop data-drivenoptimization framework, which allows the feedback from mathematical programmingto machine learning, as well as scenario-based optimization leveraging thepower of deep learning techniques. Perspectives on online learning-baseddata-driven multistage optimization with a learning-while-optimizing scheme ispresented.

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