Asia Pacific Journal of Operational Research · 2016 · 88 citations · 9 references
BusinessIndustrial EngineeringSystems EngineeringInventory ControlOptimizationSchedulingComputational ModelingSimulation MethodsSimulation ModelingSimulation OptimizationSimulation-based OptimizationSystem SimulationModel FidelityMulti-fidelity ModelingMulti-fidelity OptimizationOptimization-based SimulationOrdinal OptimizationSamplingSimulation Optimization AlgorithmsSimulation-based Stochastic Optimization
Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO 2 TOS) framework.
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