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研究生:遲銘璋
研究生(外文):Chih, Mingchang
論文名稱:模擬輸出分析之理論發展及其應用
論文名稱(外文):Methodology and Application in Simulation Output Analysis
指導教授:桑慧敏桑慧敏引用關係
指導教授(外文):Song, Wheyming Tina
學位類別:博士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:109
中文關鍵詞:模擬輸出分析
外文關鍵詞:Simulation Output Analysis
相關次數:
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A classical problem of stochastic simulation is how to estimate the variance of the sample mean from dependent but stationary outputs. Traditional estimators of the variance of the sample mean require specification of the simulation
run length a priori. To our knowledge, the dynamic non-overlapping batch means (DNBM) and dynamic partial overlapping batch means (DPBM) are the only two existing variance estimators without assuming that the simulation run
length (data size) is known in advance.
Obtaining good estimators of the variance of the sample mean without assuming that the data size is known in advance is the primary motivation of the author's dissertation research. The research encompasses five areas:
1. The creation of improving the DPBMin terms of the storage space.
2. The creation of proposing the 100(1−w−1)%DOBM, which is a generalization of DNBM and DPBM.
3. The creation of obtaining finite-memory algorithms to extend DPBM algorithm to reflect the correlation structure of the data.
4. The investigation of developing MSE-optimalDPBM algorithms to estimate the variance of the sample via estimating the optimal batch size of the estimator.
5. In addition, we apply simulation to study a physical examination service to
improve the system efficiency.
ABSTRACT
1 INTRODUCTION
1.1 Estimating the Variance of the Sample Mean
1.2 Motivation without Knowing the the run length a priori
1.3 The Five Investigations
1.4 Organization of the Dissertation
2 LITERATURE REVIEW
2.1 Batch Means Estimators
2.2 Dynamic Non-Overlapping Batch Means
2.3 Dynamic Partial-Overlapping Batch Means
3 IMPROVING THE DYNAMIC PARTIAL-OVERLAPPING BATCH MEANS
3.1 Computational Version of Dynamic Partial-Overlapping Batch Means
3.2 Summary
4 GENERALIZEDDYNAMIC PARTIAL-OVERLAPPING BATCHMEANS
4.1 A Generalized Version for Dynamic Partial-Overlapping Batch Means
4.2 The Proposed 100(1−w−1)%DOBM
4.3 Logic to form the 100(1−w−1)%DOBM
4.4 Summary
5 EXTENDED DYNAMIC PARTIAL-OVERLAPPING BATCH MEANS
5.1 Adjusting the DPBMto Reflect the Correlation Structure of the Data
5.1.1 The B-DPBM Estimator
5.1.2 Review of Analytical Minimal-Mse Linear-Combination
5.1.3 Foundation of the E-DPBM Estimator
5.2 The Extended DPBM, E-DPBM
5.2.1 Evaluation of the E-DPBM Algorithm
5.3 Summary
6 IMPLEMENTABLE MSE-OPTIMAL DPBM
6.1 Asymptotic Results
6.2 The Mse-Optimal DPBM Algorithm
6.2.1 Estimating the Optimal Batch size
6.2.2 Algorithm of Mse-Optimal DPBM
6.3 The Performance of Mse-Optimal DPBM Procedure
6.4 Summary
7 A CASE STUDY INMEDICAL SYSTEM
7.1 Problem Statement
7.2 Materials and methods
7.2.1 Simulation model
7.2.2 Goal programming
7.3 Results
7.3.1 Proposed policies
7.4 Discussion
7.5 Summary
8 SUMMARY AND FUTURE RESEARCH
8.1 Summary
8.2 Future Research
REFERENCES
APPENDIX A
APPENDIX B
APPENDIX C
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