Abstract
Simulation has primarily been used for offline static system design problems, and the simulation-based online decision making has been a weakness as the online decision epoch is tight. This work extends the scenario-dependent ranking and selection model by considering online scenario and budget. We propose a unified offline-online learning (UOOL) paradigm via simulation to find the best alternative conditional on the online scenario. The idea is to offline learn the relationship between scenarios and mean performance, and then dynamically allocates the online simulation budget based on the learned predictive model and online scenario information. The superior performance of UOOL paradigm is validated on four test functions by comparing it with artificial neural networks and decision tree.
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