Constrained optimization in simulation: efficient global optimization and Karush-Kuhn-Tucker conditions

• Published in: Journal of global optimization Vol. 91; no. 4; pp. 897 - 922
• Main Authors: Kleijnen, Jack P. C., Angün, Ebru, van Nieuwenhuyse, Inneke, van Beers, Wim C. M.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2025; Springer Nature B.V
• Subjects: Algorithms; Computer Science; Constraints; Engineering; Gaussian process; Global optimization; Kuhn-Tucker method; Literature reviews; Machine learning; Mathematical programming; Mathematics; Mathematics and Statistics; Methods; Operations Research/Decision Theory; Optimization; Real Functions; Simulation; Efficient global optimization; Gaussian process; Karush-Kuhn-Tucker conditions; Bayesian optimization; Kriging; Machine learning

We develop a novel methodology for solving constrained optimization problems in deterministic simulation. In these problems, the goal (or objective) output is to be minimized, subject to one or more constraints for the other outputs and for the inputs. Our methododology combines the“Karush-Kuhn-Tuck...