Data-driven Spatial Branch-and-bound Algorithm for Box-constrained Simulation-based Optimization

Archive 2020

In this paper, we present a novel approach that uses convex underestimators of data and a branch-and-bound procedure to obtain globally optimal solutions of simulation-based problems.

Surrogate-Based Optimization for Mixed-Integer Nonlinear Problems

Computers & Chemical Engineering 2020

Sun Hye Kim Fani Boukouvala

Computational Notebooks in Chemical Engineering Curricula

Chemical Engineering Education 2020

Jonathan Verrett Fani Boukouvala Alexander Dowling Zachary Ulissi Victor Zavala

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

Computers & Chemical Engineering 2020

Doga Demirhan Fani Boukouvala Kyungwon Kim Hyeju Song William W Tso Christodoulos A Floudas Efstratios N Pistikopoulos

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…