Likelihood ratio gradient estimation for stochastic systems
Communications of the ACM - Special issue on simulation
Stochastic approximation and optimization of random systems
Stochastic approximation and optimization of random systems
Nelder-Mead simplex modifications for simulation optimization
Management Science
Response surface methodology and its application in simulation
WSC '93 Proceedings of the 25th conference on Winter simulation
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Trust-region methods
A framework for Response Surface Methodology for simulation optimization
Proceedings of the 32nd 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
A Note on the Extended Rosenbrock Function
Evolutionary Computation
Automated response surface methodology for stochastic optimization models with unknown variance
WSC '04 Proceedings of the 36th conference on Winter simulation
Design and Analysis of Experiments
Design and Analysis of Experiments
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Variable-Number Sample-Path Optimization
Mathematical Programming: Series A and B
Better than a petaflop: the power of efficient experimental design
Proceedings of the 40th Conference on Winter Simulation
Estimating Quantile Sensitivities
Operations Research
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Experimental Methods for the Analysis of Optimization Algorithms
Experimental Methods for the Analysis of Optimization Algorithms
Work smarter, not harder: a tutorial on designing and conducting simulation experiments
Proceedings of the Winter Simulation Conference
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Response surface methodology RSM is a widely used method for simulation optimization. Its strategy is to explore small subregions of the decision space in succession instead of attempting to explore the entire decision space in a single attempt. This method is especially suitable for complex stochastic systems where little knowledge is available. Although RSM is popular in practice, its current applications in simulation optimization treat simulation experiments the same as real experiments. However, the unique properties of simulation experiments make traditional RSM inappropriate in two important aspects: 1 It is not automated; human involvement is required at each step of the search process; 2 RSM is a heuristic procedure without convergence guarantee; the quality of the final solution cannot be quantified. We propose the stochastic trust-region response-surface method STRONG for simulation optimization in attempts to solve these problems. STRONG combines RSM with the classic trust-region method developed for deterministic optimization to eliminate the need for human intervention and to achieve the desired convergence properties. The numerical study shows that STRONG can outperform the existing methodologies, especially for problems that have grossly noisy response surfaces, and its computational advantage becomes more obvious when the dimension of the problem increases.