Page 1 |
Save page Remove page | Previous | 1 of 184 | Next |
|
small (250x250 max)
medium (500x500 max)
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
Subset |
STOCHASTIC MODELS: SIMULATION AND HEAVY TRAFFIC ANALYSIS
by
Samim Ghamami
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(INDUSTRIAL AND SYSTEMS ENGINEERING)
December 2009
Copyright 2009 Samim Ghamami
Object Description
| Title | Stochastic models: simulation and heavy traffic analysis |
| Author | Ghamami, Samim |
| Author email | samim_ghamami@yahoo.com; ghamami@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Industrial & Systems Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2009-07-02 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-08-17 |
| Advisor (committee chair) | Ross, Sheldon M. |
| Advisor (committee member) |
Ward, Amy R. Moore, Jame E. |
| Abstract | This dissertation consists of two main parts: Monte Carlo Simulation and Heavy Traffic analysis. Much of the simulation part, chapter 1-3, is devoted to ways of improving simulation estimators in 3 different problems: Barrier Option Pricing, Random Knockout Tournaments, and System Reliability. Our efficiency criteria for comparing alternative estimators are variance and computing time. To achieve variance reduction, in addition to using combinations of different classical variance reduction techniques, innovative substantial sources of variance reduction are introduced by exploiting specific features of the problems under consideration. Second part of this dissertation, chapter 4-7, focuses on analyzing a stochastic control problem associated with a parallel server queueing system by employing Heavy Traffic approach. The stochastic control problem can not be solved exactly. We use heavy traffic limit theorems to derive an approximating control problem, referred to as Brownian control problem. Based on the Brownian control problem solution, we propose a control policy and then prove that our proposed policy isasymptotically optimal for the original control problem. |
| Keyword | efficient simulation; heavy traffic analysis; variance reduction; stochastic processes limits; weak convergence; Brownian motion; jump-diffusion; Markov chain Monte Carlo; Gibbs sampler; stochastic control; barrier options; queueing theory; network reliability |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2569 |
| Rights | Ghamami, Samim |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Ghamami-3182 |
| Archival file | uscthesesreloadpub_Volume26/etd-Ghamami-3182.pdf |
Description
| Title | Page 1 |
| Full text | STOCHASTIC MODELS: SIMULATION AND HEAVY TRAFFIC ANALYSIS by Samim Ghamami A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Ful llment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (INDUSTRIAL AND SYSTEMS ENGINEERING) December 2009 Copyright 2009 Samim Ghamami |
Comments
Post a Comment for Page 1
