請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41068完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Jia-Wei Yang | en |
| dc.contributor.author | 楊佳委 | zh_TW |
| dc.date.accessioned | 2021-06-14T17:14:59Z | - |
| dc.date.available | 2008-07-30 | |
| dc.date.copyright | 2008-07-30 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-25 | |
| dc.identifier.citation | [1] R. Uzsoy, C.-Y. Lee, and L. A. Martin-Vega, “A review of production planning and scheduling models in the semiconductor industry part I: System characteristics, performance evaluation and production planning,” IIE Transactions, vol. 24, no. 4, pp. 47 – 60, 1992.
[2] S. C. H. Lu, D. Ramaswamy, and P. R. Kumar, “Efficient scheduling policies to reduce mean and variance of cycle-time in semiconductor manufacturing plants,” IEEE Transactions on Semiconductor Manufacturing, vol. 7, no. 3, pp. 374–388, 1994. [3] G. K. Agrawal and S. S. Heragu, “A survey of automated material handling systems in 300-mm semiconductor fabs,” IEEE Transactions on Semiconductor Manufacturing, vol. 19, no. 1, pp. 112–120, 2006. [4] N. Bahri, J. Reiss, and B. Doherty, “A comparison of unified vs. segregated automated material handling systems for 300 mm fabs,” in IEEE International Symposium on Semiconductor Manufacturing, 2001, pp. 3–6. [5] J. T. Lin, F.-K. Wang, and C.-K. Wu, “Simulation analysis of the connecting transport AMHS in a wafer fab,” IEEE Transactions on Semiconductor Manufacturing, vol. 16, no. 3, pp. 555–564, 2003. [6] H. Kondo and M. Harada, “Study for realizing effective direct tool-to-tool delivery,” in IEEE International Symposium on Semiconductor Manufacturing, 2005, pp. 21–24. [7] J. Christopher, M. E. Kuhl, and K. Hirschman, “Simulation analysis of dispatching rules for automated material handling systems and processing tools in semiconductor fabs,” in IEEE International Symposium on Semiconductor Manufacturing, 2005, pp. 84–87. [8] D.-S. Sun, N.-S. Park, Y.-J. Lee, Y.-C. Jang, C.-S. Ahn, and T.-E. Lee, “Integration of lot dispatching and AMHS control in a 300mm wafer fab,” in IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop, 2005, pp. 270–274. [9] M. E. Pfund, H. Balasubramanian, J. W. Fowler, S. J. Mason, and O. Rose, “A multi-criteria approach for scheduling semiconductor wafer fabrication facilities,” Journal of Scheduling, vol. 11, no. 1, pp. 29–47, 2008. [10] C. A. Coello Coello, “Evolutionary multi-objective optimization: a historical view of the field,” IEEE Computational Intelligence Magazine, vol. 1, no. 1, pp. 28–36, 2006. [11] C. Dimopoulos, “A review of evolutionary multiobjective optimization applications in the area of production research,” in Congress on Evolutionary Computation, 2004, pp. 1487–1494. [12] M. Pfund, S. Mason, and J. Fowler, “Semiconductor manufacturing scheduling and dispatching,” in Handbook of Production Scheduling. Springer, 2006, ch. 9, pp. 213–241. [13] L. Wein, “On the relationship between yield and cycle time in semiconductor wafer fabrication,” IEEE Transactions on Semiconductor Manufacturing, vol. 5, no. 2, pp. 156–158, 1992. [14] Y.-D. Kim, J.-U. Kim, S.-K. Lim, and H.-B. Jun, “Due-date based scheduling and control policies in a multiproduct semiconductor wafer fabrication facility,” IEEE Transactions on Semiconductor Manufacturing, vol. 11, no. 1, pp. 155– 164, 1998. [15] Y.-D. Kim, J.-G. Kim, B. Choi, and H.-U. Kim, “Production scheduling in a semiconductor wafer fabrication facility producing multiple product types with distinct due dates,” IEEE Transactions on Robotics and Automation, vol. 17, no. 5, pp. 589–598, 2001. [16] R. Dabbas and J. Fowler, “A new scheduling approach using combined dispatching criteria in wafer fabs,” IEEE Transactions on Semiconductor Manufacturing, vol. 16, no. 3, pp. 501–510, 2003. [17] N. Bahaji and M. E. Kuhl, “A simulation study of new multi-objective composite dispatching rules, CONWIP, and push lot release in semiconductor fabrication,” International Journal of Production Research, no. 1, pp. 1 – 24, 2007. [18] Z. Wang, Q. Wu, and F. Qiao, “A lot dispatching strategy integrating WIP management and wafer start control,” IEEE Transactions on Automation Science and Engineering, vol. 4, no. 4, pp. 579–583, 2007. [19] J.-H. Chen, L.-C. Fu, M.-H. Lin, and A.-C. Huang, “Petri-net and GA-based approach to modeling, scheduling, and performance evaluation for wafer fabrication,” IEEE Transactions on Robotics and Automation, vol. 17, no. 5, pp. 619–636, 2001. [20] M. Liu and C. Wu, “Genetic algorithm using sequence rule chain for multi-objective optimization in re-entrant micro-electronic production line,” Robotics and Computer-Integrated Manufacturing, vol. 20, no. 3, pp. 225–236, 2004. [21] P. J. Egbelu and J. M. A. Tanchoco, “Characterization of automatic guided vehicle dispatching rules,” International Journal of Production Research, vol. 22, no. 3, p. 359, 1984. [22] D.-Y. Liao and H.-S. Fu, “Speedy delivery -dynamic OHT allocation and dispatching in large-scale, 300-mm AMHS management,” IEEE Robotics & Automation Magazine, vol. 11, no. 3, pp. 22–32, 2004. [23] B.-I. Kim, S. Oh, J. Shin, M. Jung, J. Chae, and S. Lee, “Effectiveness of vehicle reassignment in a large-scale overhead hoist transport system,” International Journal of Production Research, vol. 45, no. 4, pp. 789 – 802, 2007. [24] N. Bahri and R. J. Gaskins, “Automated material handling system traffic control by means of node balancing,” in Proceedings of the 32nd conference on Winter simulation, 2000, pp. 1344–1346. [25] H.-S. Min and Y. Yih, “Selection of dispatching rules on multiple dispatching decision points in real-time scheduling of a semiconductor wafer fabrication system,” International Journal of Production Research, vol. 41, no. 16, pp. 3921 – 3941, 2003. [26] J. C. Tyan, T. C. Du, J. C. Chen, and I. H. Chang, “Multiple response optimization in a fully automated FAB: an integrated tool and vehicle dispatching strategy,” Computers & Industrial Engineering, vol. 46, no. 1, pp. 121–139, 2004. [27] X. Yao, E. Fernandez-Gaucherand, M. Fu, and S. Marcus, “Optimal preventive maintenance scheduling in semiconductor manufacturing,” IEEE Transactions on Semiconductor Manufacturing, vol. 17, no. 3, pp. 345–356, 2004. [28] L. Foster, “300mm wafer factory automation and the logistics infrastructure challenge,” Future Fab International, 2001. [29] J. Ferrell and M. Pratt, I300I Factory Guidelines: Version 5.0, International SEMATECH, 2002. [30] M. R. Garey, D. S. Johnson, and R. Sethi, “The complexity of flowshop and jobshop scheduling,” Mathematics of Operations Research, vol. 1, no. 2, pp. 117–129, 1976. [31] H. Gurnani, R. Anupindi, and R. Akella, “Control of batch processing systems in semiconductor wafer fabrication facilities,” IEEE Transactions on Semiconductor Manufacturing, vol. 5, no. 4, pp. 319–328, 1992. [32] J. Holland, Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, 1975. [33] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, 1989. [34] R. Cheng, M. Gen, and Y. Tsujimura, “A tutorial survey of job-shop scheduling problems using genetic algorithms–I. representation,” Computers & Industrial Engineering, vol. 30, pp. 983–997, 1996. [35] J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, 1992. [36] E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the Strength Pareto Evolutionary Algorithm,” Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Zurich, Switzerland, Tech. Rep. 103, 2001. [37] J. Knowles and D. Corne, “Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy,” Evolutionary Computation, vol. 8, no. 2, pp. 149–172, 2000. [38] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. [39] C. Dimopoulos and A. M. S. Zalzala, “Investigating the use of genetic programming for a classic one-machine scheduling problem,” Advances in Engineering Software, vol. 32, no. 6, pp. 489–498, 2001. [40] W.-J. Yin, M. Liu, and C. Wu, “Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming,” in Congress on Evolutionary Computation, vol. 2, 2003, pp. 1050–1055. [41] N. B. Ho and J. C. Tay, “Evolving dispatching rules for solving the flexible job-shop problem,” in IEEE Congress on Evolutionary Computation, vol. 3, 2005, pp. 2848–2855. [42] C. D. Geiger and R. Uzsoy, “Learning effective dispatching rules for batch processor scheduling,” International Journal of Production Research, vol. 46, no. 6, pp. 1431 – 1454, 2008. [43] J. Y. Yen, “Finding the k shortest loopless paths in a network,” Management Science, vol. 17, no. 11, pp. 712–716, 1971. [44] E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, pp. 269–271, 1959. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41068 | - |
| dc.description.abstract | 在這篇論文中，我們解決了存在於十二吋晶圓廠的兩個問題。首先，對於批貨排程問題，我們提出一個基於遺傳規劃的多目標法則產生器，來發展出有用的派工法則，進而可提供近似最佳且考量多個目標的批貨排程表。其次，我們考慮到懸吊式搬運車路徑選擇的問題。而這個問題起因於現代的自動化物料搬運系統，其能夠達到機台與機台之間的直接傳輸，而導致懸吊式搬運車交通的擠塞較過去常發生。為了解決自動化物料搬運系統之交通擠塞，我們提出一個動態路徑選擇的方法，這個方法能夠找到近似於最短且最不擠塞的路徑供搬運車行走。這個方法藉由適應於動態的交通環境，使得能夠減少交通擠塞且能達到快速的批貨運送。最後，我們整合了提出的多目標法則產生器和動態路徑選擇方法，以改進廠區的兩個效能指標：平均生產週程時間、批貨延期率。從實驗的結果可顯示出所提出的方法之有效性。 | zh_TW |
| dc.description.abstract | In this thesis, we solve two problems in a 300-mm wafer fabrication facility (fab). Firstly, for the lot scheduling problem, we propose a multi-objective genetic programming based rule generator (MOGPRG) to evolve useful dispatching rules, which can provide near-optimal lot schedules concerning multiple objectives. Secondly, the overhead hoist transports (OHT) routing problem is considered. As the modern automated material handling system (AMHS) is capable of doing tool-to-tool direct delivery, the congestion of OHTs may happen more often than the past. To deal with the traffic congestion in AMHS, a dynamic routing method is proposed to find the near-shortest and less-congested path for the OHT to travel along. It can reduce the traffic congestion and achieve fast lot delivery by adapting to the dynamic traffic environment. The proposed MOGPRG is integrated with the dynamic routing method to improve two fab performance metrics: mean cycle time and tardy rate. Experimental results show the effectiveness of the proposed MOGPRG and dynamic routing method. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-14T17:14:59Z (GMT). No. of bitstreams: 1 ntu-97-R95922076-1.pdf: 677164 bytes, checksum: 3ae9f21e8fc0394f2c5a09b1b9ddb0b9 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | List of Figures vii
List of Tables viii 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 Semiconductor Manufacturing Environment 9 2.1 Overview of Semiconductor Manufacturing Systems . . . . . . . . . . 9 2.2 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1 Lot Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 Batch Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.3 OHT Dispatching . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.4 OHT Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.5 Multiobjective Optimization . . . . . . . . . . . . . . . . . . . 16 3 Genetic Programming-based Multi-objective Lot Scheduling 18 3.1 Overview of Evolutionary Algorithm . . . . . . . . . . . . . . . . . . 18 3.1.1 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Genetic Programming . . . . . . . . . . . . . . . . . . . . . . 20 3.1.3 Multi-objective Evolutionary Algorithm (MOEA) . . . . . . . 21 3.2 Basic Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3 Encoding and Decoding Schemes . . . . . . . . . . . . . . . . . . . . 23 3.3.1 Encoding Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.2 Decoding Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Multi-objective Fitness Assignment . . . . . . . . . . . . . . . . . . . 27 3.4.1 Population Classification . . . . . . . . . . . . . . . . . . . . . 28 3.4.2 Crowding Distance Calculation . . . . . . . . . . . . . . . . . 30 3.4.3 Crowding Distance Normalization . . . . . . . . . . . . . . . . 31 3.4.4 Final Fitness Assignment . . . . . . . . . . . . . . . . . . . . . 31 3.4.5 Illustrative Example . . . . . . . . . . . . . . . . . . . . . . . 32 3.5 The Procedure of Multi-objective Genetic Programming-based Rule Generator (MOGPRG) . . . . . . . . . . . 33 3.5.1 Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.5.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.5.3 Mating Selection . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.5.4 Crossover and Mutation . . . . . . . . . . . . . . . . . . . . . 35 3.5.5 Environmental Selection . . . . . . . . . . . . . . . . . . . . . 36 3.5.6 Termination Condition . . . . . . . . . . . . . . . . . . . . . . 37 4 Dynamic OHT Routing 38 4.1 Overview of Routing Method . . . . . . . . . . . . . . . . . . . . . . 38 4.2 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.3 On-line Paths Collection . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.4 On-line Path Decision . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5 Experiments and Results 45 5.1 The Wafer Fab Model . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.4 Performance of Proposed Dynamic OHT Routing Method . . . . . . . 50 5.5 Performance of Proposed MOGPRG . . . . . . . . . . . . . . . . . . 51 5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 6 Conclusions and Future Work 55 Reference 57 | |
| dc.language.iso | en | |
| dc.subject | 多目標演化式演算法 | zh_TW |
| dc.subject | 生產排程 | zh_TW |
| dc.subject | 遺傳規劃 | zh_TW |
| dc.subject | 派工法則 | zh_TW |
| dc.subject | 懸吊式搬運車之路徑選擇 | zh_TW |
| dc.subject | dispatching rules | en |
| dc.subject | production scheduling | en |
| dc.subject | genetic programming | en |
| dc.subject | overhead hoist transport (OHT) routing | en |
| dc.subject | Multiobjective evolutionary algorithm | en |
| dc.title | 十二吋晶圓廠之多目標批貨排程與懸吊式搬運車之動態路徑選擇 | zh_TW |
| dc.title | Multiobjective Lot Scheduling and Dynamic OHT Routing in a 300-mm Wafer Fab | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張時中,陳正剛,曹承礎,陳文耀 | |
| dc.subject.keyword | 多目標演化式演算法,生產排程,遺傳規劃,派工法則,懸吊式搬運車之路徑選擇, | zh_TW |
| dc.subject.keyword | Multiobjective evolutionary algorithm,production scheduling,genetic programming,dispatching rules,overhead hoist transport (OHT) routing, | en |
| dc.relation.page | 60 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-07-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-97-1.pdf 未授權公開取用 | 661.29 kB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。