A review of simulation optimization techniques
Proceedings of the 30th conference on Winter simulation
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Gradient-based simulation optimization
Proceedings of the 38th conference on Winter simulation
A testbed of simulation-optimization problems
Proceedings of the 38th conference on Winter simulation
Enhancing business process management with simulation optimization
Proceedings of the 38th conference on Winter simulation
Simulation optimization with hybrid golden region search
Winter Simulation Conference
Proceedings of the Winter Simulation Conference
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Simulation optimization (SO) is the process of finding the optimum design of a system whose performance measure(s) are estimated via simulation. We propose some ideas to improve overall efficiency of the available SO methods and develop a new approach that primarily deals with continuous two dimensional problems with bounded feasible region. Our search based method, called Adaptive Partitioning Search (APS), uses a neural network as meta-model and combines various exploitation strategies to locate the optimum. Our numerical results show that in terms of the number of evaluations (simulation runs) needed, the APS algorithm converges much faster to the optimum design than two well established methods used as benchmark.