Thursday, September 19, 2019

Thursday, September 19, 2019 — 4:00 PM EDT

Simulation Optimization under Input Model Uncertainty

Simulation optimization is concerned with identifying the best solution for large, complex and stochastic physical systems via computer simulation models. Its applications span across various fields such as transportation, finance, power, and healthcare. A stochastic simulation model is driven by a set of distributions, known as “input model”. However, since these distributions are usually estimated using finite real-world data, the simulation output is subject to the so-called “input model uncertainty”. Ignoring input uncertainty can cause a higher risk of selecting an inferior solution in simulation optimization. In this talk, I will first present a new framework called Bayesian Risk Optimization (BRO) that hedges against the risk of input uncertainty in simulation optimization. Then I will focus on the problem of optimizing over a finite solution space, a problem known as Ranking and Selection in statistics literature, or Best-Arm Identification in Multi-Armed Bandits literature, and present two new algorithms that can handle input uncertainty. 

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