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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳正剛 | |
dc.contributor.author | Ling-Cheng Chang | en |
dc.contributor.author | 張凌誠 | zh_TW |
dc.date.accessioned | 2021-06-15T00:44:30Z | - |
dc.date.available | 2018-06-30 | |
dc.date.copyright | 2008-09-02 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-08-27 | |
dc.identifier.citation | 1. Atkinson, A.C. and R.D. Cook, D-Optimum Designs for Heteroscedastic Linear Models. Journal of the American Statistical Association, 1995. 90(429).
2. Atkinson, A.C. and A.N. Donev, Optimal Experimental Designs. SIAM Journal of Algebraic and Discrete Methods, 1987. 8(2): p. 277-284. 3. Atkinson, A.C. and A.N. Donev, The construction of exact D-optimum experimental designs with application to blocking response surface designs. Biometrika, 1989. 76(3): p. 515-526. 4. Box, G.E.P. and N.R. Draper, Robust designs. Biometrika, 1975. 62(2): p. 347-352. 5. Casella, G. and R.L. Berger, Statistical inference. 2002: Thomson Learning Pacific Grove, CA. 6. Chen, C.H., et al., Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization. Discrete Event Dynamic Systems, 2000. 10(3): p. 251-270. 7. Chen, H. and E. Yucesan, Computing efforts allocation for ordinal optimization and discreteevent simulation. Automatic Control, IEEE Transactions on, 2000. 45(5): p. 960-964. 8. Cheng, R.C.H. and J.P.C. Kleijnen, Improved design of queueing simulation experiments with highly heteroscedastic responses. Operations Research, 1999. 47(5): p. 762-777. 9. Fedorov, V.V., Theory of optimal experiments. 1972: Academic Press, New York. 10. Haines, L.M., The application of the annealing algorithm to the construction of exact optimal designs for linear-regression models. Technometrics, 1987. 29(4): p. 439-447. 11. Hu, M.D. and S.C. Chang, Translating overall production goals into distributed flow control parameters for semiconductor manufacturing. Journal of manufacturing systems, 2003. 22(1): p. 46-63. 12. Kiefer, J., Optimum experimental designs. Journal of the Royal Statistical Society, Series B, 1959. 21: p. 272-304. 13. Kleinrock, L., Queueing Systems, Vol. 11: Computer Applications. 1976, Wiley Interscience, New York. 14. Rinott, Y., On two-stage selection procedures and related probability-inequalities. Communications in Statistics-Theory and Methods, 1978. 7(8): p. 799-811. 15. Welch, W.J., Branch-and-Bound Search for Experimental Designs Based on D Optimality and Other Criteria. TECHNOMETRICS., 1982. 24(1): p. 41-48. 16. Whitt, W., Planning queueing simulations. Management Science, 1989. 35(11): p. 1341-1366. 17. Whitt, W., Approximations for the GI/G/m Queue. Production and Operations Management, 1993. 2(2): p. 114-161. 18. Yang, F., B. Ankenman, and B.L. Nelson, Efficient generation of cycle time-throughput curves through simulation and metamodeling. Naval Research Logistics, 2007. 54(1): p. 78-93. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42060 | - |
dc.description.abstract | 在一個G/G/m系統中,尋找一組參數組合使得系統的期望等待時間最短是很重要的。迴歸模型通常被建立用來描述參數組合跟期望等待時間的關係,希望藉此來尋找最佳的參數組合。過去的文獻中,有人提出一系列的方法來決定該在哪裡做實驗以及實驗次數使得迴歸模型估計出來值的變異最小,但是過去的方法使用的模型侷限在單一變數,只能探討利用率跟等候時間的關係。因此,我們希望提出一種新的方法—模型基礎計算資源分配(Model-based Computing Budget Allocation),這個方法結合了等候理論以及最佳化實驗設計理論,用來解決多個任意變數的計算資源分配問題。我們的方法根據等候理論,發展出一個新的模型來描述期望等待時間跟任意變數間的關係,然後根據D-最佳化實驗的觀念,為了使回歸模型的一般化變異最小,決定計算資源的分配。為了驗証我們方法,我們用了兩個例子,第一個例子是在G/G/1系統搜尋一組難以找到最佳參數的例子,第二個例子則是延續第一個例子,額外加進了另一個二元因子,代表不同的派工法則,跟另一個方法OCBA比較後,可以發現我們的方法能夠獲得較高的正確選擇機率,在相同的模擬資源下。 | zh_TW |
dc.description.abstract | Parameter setting to minimize the expected waiting time in G/G queue systems is an important issue. Regression models are constructed to describe the relationship between the expected waiting time and the parameter setting to search for the optimal setting. In the literature, Cheng and Kleijnen, Yang, Ankenman and Nelson have proposed procedures to choose setting levels needed to be simulated and the number of replication for each level. However, their models consider only one decision variable, i.e., the traffic intensity rate or the throughput rate. We propose a procedure, referred to as Model-based Computing Budget Allocation (MCBA), which combines the queuing theory and the optimum design of experiment to solve the budget allocation problem with multiple decision variables. Our approach approximates the expected waiting time with polynomial functions based on formulas developed in queuing theories and sequentially decides which parameter settings are needed to be simulated based on the concept of D-optimality. To verify the performance of MCBA, we study two cases. The first case is a G/G/1 queuing problem with the optimal parameter setting difficult to determine. The second case has an additional binary decision variable representing two different dispatching rules. Compared with the results of Optimal Computing Budget Allocation (OCBA), the proposed approach is observed to achieve higher probability of correct selection under the same simulation cost. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T00:44:30Z (GMT). No. of bitstreams: 1 ntu-97-R95546012-1.pdf: 655711 bytes, checksum: ef9e54d6abd7bf9f7a7ae3be4e9209f3 (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | 誌謝 i
論文摘要 i Abstract i Content ii Contents of Figures iv Content of Table v 1. Introduction 1 1.1. Background 1 1.2. Problem statement 3 1.3. Literature review 5 1.3.1. Model-free method 6 1.3.2. Model-based method 8 1.4. Research objective 23 1.5. Thesis structure 24 2. General model for expected waiting time 26 2.1. General empirical model 26 2.2. D-criteria for general empirical model 29 2.3. Empirical models and corresponding D-criteria 33 2.3.1. M/M/1 queue 33 2.3.2. G/G/1 queue with heavy traffic 35 3. Model-based Computing Budget Allocation 38 3.1. D-optimality 38 3.1.1. The exact design and the continuous design 38 3.1.2. Candidate list 39 3.1.3. Exchange algorithm 41 3.2. Procedure for Model-based Computing Budget Allocation (MCBA) 42 3.3. Example 44 4. Numerical Test 49 4.1. Experiment 1 50 4.2. Experiment 2 56 5. Conclusion 63 Reference 66 | |
dc.language.iso | en | |
dc.title | G/G 等候系統模擬次數分配問題之模型基礎法 | zh_TW |
dc.title | Model-based Computing Budget Allocation for G/G Queue System Simulations | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 陳俊宏 | |
dc.contributor.oralexamcommittee | 任恒毅,蔣明晃,張時中,陳靜枝 | |
dc.subject.keyword | 模擬,OCBA,信息矩陣,D最佳化實驗設計因子,G/G佇列, | zh_TW |
dc.subject.keyword | Simulation,OCBA,Information Matrix,D-criteria,G/G queue, | en |
dc.relation.page | 67 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2008-08-27 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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