Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/47483
Title: SIMULATION OPTIMIZATION USING OPTIMAL COMPUTING BUDGET ALLOCATION
Authors: XIAO HUI
Keywords: simulation optimization, ranking and selection, optimal computing budget allocation, quadratic regression, search algorithms, complete ranking
Issue Date: 28-Jun-2013
Source: XIAO HUI (2013-06-28). SIMULATION OPTIMIZATION USING OPTIMAL COMPUTING BUDGET ALLOCATION. ScholarBank@NUS Repository.
Abstract: Optimal computing budget allocation (OCBA) aims to determine the most efficient way of using the fixed limited simulation budget in order to obtain the best simulation results. In this thesis, the OCBA framework is extended to ranking-based OCBA and regression-based OCBA. Ranking-based OCBA aims to derive the simulation budget allocation rules for ranking all alternatives completely and ranking the top m designs. Regression-based OCBA considers the budget allocation rule when the simulation output can be modeled by quadratic regression functions. In general, efficient simulation budget allocation rules are proposed for ranking-based problems and regression-based problems in this thesis. The results of this thesis can be applied to general simulation optimization problems.
URI: http://scholarbank.nus.edu.sg/handle/10635/47483
Appears in Collections:Ph.D Theses (Open)

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