Response surfaces: designs and analyses
Response surfaces: designs and analyses
Analysis of sample-path optimization
Mathematics of Operations Research
Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Trust-region methods
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Automated response surface methodology for stochastic optimization models with unknown variance
WSC '04 Proceedings of the 36th conference on Winter simulation
Efficient experimental design tools for exploring large simulation models
Computational & Mathematical Organization Theory
Better than a petaflop: the power of efficient experimental design
Winter Simulation Conference
Simulation optimization using metamodels
Winter Simulation Conference
Better than a petaflop: the power of efficient experimental design
Proceedings of the Winter Simulation Conference
Combining strong and screening designs for large-scale simulation optimization
Proceedings of the Winter Simulation Conference
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Response Surface Methodology (RSM) is a metamodel-based optimization method. Its strategy is to explore small subregions of the parameter space in succession instead of attempting to explore the entire parameter space directly. This method has been widely used in simulation optimization. However, RSM has two significant shortcomings: Firstly, it is not automated. Human involvements are usually required in the search process. Secondly, RSM is heuristic without convergence guarantee. This paper proposes Stochastic Trust Region Gradient-Free Method (STRONG) for simulation optimization with continuous decision variables to solve these two problems. STRONG combines the traditional RSM framework with the trust region method for deterministic optimization to achieve convergence property and eliminate the requirement of human involvement. Combined with appropriate experimental designs and specifically efficient screening experiments, STRONG has the potential of solving high-dimensional problems efficiently.