Response surface methodology with stochastic constraints for expensive simulation

• Published in: The Journal of the Operational Research Society Vol. 60; no. 6; pp. 735 - 746
• Main Authors: Angün, E, Kleijnen, J, den Hertog, D, Gürkan, G
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.06.2009; Palgrave Macmillan; Palgrave Macmillan UK; Taylor & Francis Ltd
• Subjects: Applied sciences; Approximation; bootstrap; Business and Management; Case studies; Case-Oriented Paper; Case-Oriented Papers; Cost estimates; Determinism; Estimation methods; Exact sciences and technology; Experimental design; Heuristic; Heuristics; interior point methods; Interior points; Management; Mathematical programming; Mathematics; Noise; Objective functions; Operational research and scientific management; Operational research. Management science; Operations research; Operations Research/Decision Theory; Optimal solutions; Optimization; Point estimators; Polynomials; Probability and statistics; Sciences and techniques of general use; Simulation; Statistics; stochastic optimization; Studies; interior point methods; bootstrap; simulation; stochastic optimization; Steepest descent method; Interior point method; Software package; Affine transformation; Optimization; Stochastic programming; Scale transformation; Heuristic method; Bootstrap; Response surface; Objective function
• ISSN: 0160-5682; 1476-9360
• DOI: 10.1057/palgrave.jors.2602614

This article investigates simulation-based optimization problems with a stochastic objective function, stochastic output constraints, and deterministic input constraints. More specifically, it generalizes classic response surface methodology (RSM) to account for these constraints. This Generalized RSM-abbreviated to GRSM-generalizes the estimated steepest descent-used in classic RSM-applying ideas from interior point methods, especially affine scaling. This new search direction is scale independent, which is important for practitioners because it avoids some numerical complications and problems commonly encountered. Furthermore, the article derives a heuristic that uses this search direction iteratively. This heuristic is intended for problems in which simulation runs are expensive, so that the search needs to reach a neighbourhood of the true optimum quickly. The new heuristic is compared with OptQuest, which is the most popular heuristic available with several simulation software packages. Numerical illustrations give encouraging results.