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.

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…