Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/49285
Title: ENHANCING SIMULATION OPTIMIZATION METHODS USING SMOOTHING AND METAMODELING TECHNIQUES
Authors: MA SICONG
Keywords: Simulation optimization, direct search methods, probability optimization, local metamodels, smoothing, retrospective-approximation algorithms
Issue Date: 16-Aug-2013
Source: MA SICONG (2013-08-16). ENHANCING SIMULATION OPTIMIZATION METHODS USING SMOOTHING AND METAMODELING TECHNIQUES. ScholarBank@NUS Repository.
Abstract: Simulation optimization is widely applied and can be found in many fields with the development of computer technology. However, there are many issues arise in dealing with simulation optimization problems, which make them generally more difficult to solve compared with the ordinary optimization problems. One of the main issues is that the simulation programs could be extremely expensive and time consuming. Also, the form of the objective function is unknown and the response value returned by simulation experiment posses random noise. The purpose of this study is to explore enhanced simulation algorithms using smoothing and metamodeling techniques to provide users with new options to deal with simulation optimization problems.
URI: http://scholarbank.nus.edu.sg/handle/10635/49285
Appears in Collections:Ph.D Theses (Open)

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