Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/35878
Title: Efficient computing budget allocation by using regression with sequential sampling constraint
Authors: HU XIANG
Keywords: transient performance, sequential constraint, Ranking & Selection
Issue Date: 21-Aug-2012
Source: HU XIANG (2012-08-21). Efficient computing budget allocation by using regression with sequential sampling constraint. ScholarBank@NUS Repository.
Abstract: In this thesis, we develop an efficient computing budget allocation rule to run simulation for a single design whose transient mean performance follows a certain underlying functional form, which enables us to obtain more accurate estimation of design performance by doing regression. A sequential sampling constraint is imposed so as to fully utilize the information along the simulation replication. We formulate this problem using the Bayesian regression framework and solve it for some simple underlying functions under a few common assumptions in the literature of regression analysis. Consequently, the Single Design Budget Allocation (SDBA) Procedure that determines the number of simulation replications and corresponding run lengths given a certain computing budget is developed. Moreover, based on the SDBA Procedure, the SDBA+OCBA Procedure has been developed, which is proved to be an efficient computing budget allocation rule that enables us to correctly select the best design among several alternative designs.
URI: http://scholarbank.nus.edu.sg/handle/10635/35878
Appears in Collections:Master's Theses (Open)

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