Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/230679
Title: EFFICIENT SAMPLING BUDGET ALLOCATION FOR ONLINE DECISION MAKING
Authors: LIU HAITAO
Keywords: Simulation; Ranking and selection; Feasibility determination; Offline simulation online application
Issue Date: 6-Apr-2022
Citation: LIU HAITAO (2022-04-06). EFFICIENT SAMPLING BUDGET ALLOCATION FOR ONLINE DECISION MAKING. ScholarBank@NUS Repository.
Abstract: Often, executing simulation experiments is time-consuming, which hinders its application in real-time decisions. We firstly consider a problem where observations are censored. We formulate it as ranking and selection with censored observations and propose a convergent sampling algorithm. Then, we allow performance to vary with scenarios, where outcomes can be predicted via a logistic model. The goal is to identify feasible alternatives conditional on scenarios. An information gradient policy is developed to allocate offline simulation budget. Lastly, we consider a short time after observing each online scenario and propose a unified offline and online learning paradigm to select the best conditional on online scenarios. We model the mean performance as a function of scenarios and learn a predictive model based on simulation observations. Then, we develop a sequential sampling method to allocate online simulation budget. The superior performance of proposed approaches is demonstrated on synthetic functions and real-world applications.
URI: https://scholarbank.nus.edu.sg/handle/10635/230679
Appears in Collections:Ph.D Theses (Open)

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