Nelder-Mead simplex modifications for simulation optimization
Management Science
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Trust-region methods
Proceedings of the 32nd conference on Winter simulation
Proceedings of the 33nd conference on Winter simulation
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Two-phase screening procedure for simulation experiments
ACM Transactions on Modeling and Computer Simulation (TOMACS)
INFORMS Journal on Computing
Winter Simulation Conference
| Hi-index | 0.00 |
Simulation optimization has received a great deal of attention over the decades, which probably can be attributed to its generality and solvability in many practical problems. On the other hand, simulation optimization is well-recognized as a difficult problem, especially when the problem dimensionality grows. STRONG is a newly-developed method built upon the traditional response surface methodology. Its advantages lie in the automation and provable convergence, as opposed to traditional RSM that requires human involvements and the final solution has no quality guarantee. Moreover, the use of efficient experimental design and regression analysis grants STRONG the great potential to deal with large-scale problems. This paper exploits the basic structure of STRONG and integrates an efficient screening design to handle problems that are of realistic scale, i.e., hundreds of factors. The convergence of the new algorithm is proved. The computational advantage is shown by numerical evaluations.