An integrated data-driven modeling & global optimization approach for multi-period nonlinear production planning problems

Computers & Chemical Engineering 2020

In this work, we present an integrated data-driven modeling and global optimization-based multi-period nonlinear production planning framework that is applied to a real-life refinery complex. The proposed multi-period framework significantly extends and improves previous works based on single-period planning formulation…

Managing Uncertainty in Data-Driven Simulation-Based Optimization

Computers & Chemical Engineering 2019

Gordon Hullen Jianyuan Zhai Sun Hye Kim Anshuman Sinha Matthew Realff Fani Boukouvala

Optimization using data from complex simulations has become an attractive decision-making option, due to ability to embed high-fidelity, non-linear understanding of processes within the search for optimal values. Due to lack of tractable algebraic equations, the link between simulations and…

Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques

Optimization Letters 2019

Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear…