請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/24389
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃漢邦 | |
dc.contributor.author | Chiao-Ju Tsai | en |
dc.contributor.author | 蔡蕎如 | zh_TW |
dc.date.accessioned | 2021-06-08T05:24:13Z | - |
dc.date.copyright | 2005-07-27 | |
dc.date.issued | 2005 | |
dc.date.submitted | 2005-07-24 | |
dc.identifier.citation | [1] A. Adam, E. Rivlin, and I. Shimshoni, “ROR: Rejection of Outliers by Rotations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 1, pp. 78-84, January 2001.
[2] G. Andreatta, L. Deserti, and L. N. Giraldo, “Scheduling Algorithms for a Two-Machine Flexible Manufacturing System,” International Journal of Flexible Manufacturing Systems,” Vol. 7, No. 3, pp. 207-227, 1995. [3] Y. Arzi, and L. Iaroslavitz, “Operating an FMC by a Decision-Tree-based Adaptive Production Control System,” International Journal of Production Research, Vol. 38, pp. 675-697, 2000. [4] H. Aytug, G. J. Koehler, and J. L. Snowdon, “A Review of Machine Learning in Scheduling,” IEEE Transactions on Engineering Management, Vol. 41, No. 2, pp. 165-171, 1994. [5] U. Bader, A. Besler, J. Schliesser, and J. Dorner, “A View to the Future-a Modular Simulation Kit for Cost-Efficient Fab-Wide Planning and the Simulation of Automated Material Handling Systems in the Semiconductor Industry,” IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp. 109-112, 1999. [6] X. Bai, N. Srivatsan, and S. Gershwin, “Hierarchical Real-Time Scheduling of a Semiconductor Fabrication Facility,” Proceedings, 9th IEEE International Manufacturing Technology Symposium, Washington, DC, pp. 312-317, 1990. [7] A. Bauer, R. Bowden, J. Browse, J. Duggan, and G. Lyons, Shop Floor Control System - from Design to Implementation, London: Chapman & Hall, pp 60-66, 1991. [8] P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using class Specific Linear Projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, July 1997. [9] E. Campbell, and J. Ammenheuser, “300 mm Factory Layout and Material Handling Modeling: Phase II Report,” International SEMATECH, Tech Transfer Document # 99113848B-ENG, February 2000. [10] P. Campbell, and G. Laitinen, “Overhead Interbay Automation and Micrestocking-a Virtual Fab Case Study,” Proceedings of the IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop, Cambridge, MA., pp. 368-372, June 1997. [11] P. L. Campbell, and E. A. Macnair, “A Model of a 300 mm Wafer Fabrication Line,” Proceedings of the 1999 Winter Simulation Conference, Phoenix, Arizona, 1999. [12] P. Campbell, and M. Norman, “Microstocking and Fab Throughput,” Proceedings of the AutoSimulations ’98 Symposium, Bountiful, UT., pp. 101-106, 1998. [13] F. T. S. Chan, H. K. Chan, H. C. W. Lau, and R. W. L. Ip, “Analysis of Dynamic Dispatching Rules for a Flexible Manufacturing System,” Journal of Materials Processing Technology, Vol. 138, pp. 325–331, August 2003. [14] M. Chase, Full-Fab Automation Challenges, Semiconductor International, pp. 152, July 2000. [15] J. H. Chiang, and P. D. Gader, “Hybrid Fuzzy-Neural Systems in Handwritten Word Recognition,” IEEE Transactions on Fuzzy Systems, Vol. 5, No. 4, pp. 497-510, November 1997. [16] H. Cho, and R. A. Wysk, “A Robust Adaptive Scheduler for an Intelligent Workstation Controller,” International Journal of Production Research, Vol. 31, pp. 771-789, 1993. [17] W. Conway, W. L. Maxwell, and L. W. Miller, Theory of Scheduling, Addison Wesley, New York, 1966. [18] K. Finkenzeller, RFID-Handbook, 2nd Eddition, Wiley & Sons Ltd., 2003. [19] K. Fordyce et al., “Logistics Management System: An Advanced Decision Support System for the Fourth Decision Tier Dispatch or Short-Interval Scheduling,” Production and Operations Management, Vol. 1, pp. 70-86, 1992. [20] C. F. Frank, H. Jiankun, and M. A. Centeno, “Intelligent Scheduling and Control of Rail-Guided Vehicles and Load/Unload Operations in a Flexible Manufacturing System,” Journal of Intelligent Manufacturing, Vol. 10, No. 5, pp. 405-421, 1999. [21] M. Gen and R. Cheng, Genetic Algorithms & Engineering Design, John Wiley & Sons, Inc.,July 1997. [22] A. Giua, M. T. Pilloni, and C. Seatzu, “Modeling and Simulation of a Bottling Plant Using Hybrid Petri Nets,” International Journal of Production Research, Vol. 43, No. 7, pp. 1375–1395, April 2005. [23] S. K. Goyal, K. Mehta, R. Kodali, and S. G. Deshmukh, “Simulation for Analysis of Scheduling Rules for a Flexible Manufacturing System,” Integrated Manufacturing Systems, Vol. 6, No. 5, pp. 21-26, 1995. [24] H. P. Hillion, and J. M. Proth, “Performance Evaluation of Job-Shop Systems Using Timed Event–Graphs,” IEEE Transactions on Automatic Control, Vol. 34, No. 1, pp. 3-9, January, 1989. [25] C. W. Hsu, C. C. Chang, and C. J. Lin, “A Practical Guide to Support Vector Classification,” Technical report, 2003. [26] W. H. Ip, K. L. Yung, H. Min, and D. Wang, “A CONWIP Model for FMS Control,” Journal of Intelligent Manufacturing, Vol. 13, No. 2, pp. 109-117, 2002. [27] M. Jahangirian, and G. V. Conroy, “Intelligent Dynamic Scheduling System: the Application of Genetic Algorithms,” Integrated Manufacturing Systems, Vol. 11, No. 4, pp. 247-257, 2000. [28] V. Jain, R. Swarnkar, and M. K. Tiwari, “Modelling and Analysis of Wafer Fabrication Scheduling Via Generalized Stochastic Petri Net and Simulated Annealing,” International Journal Production Research, Vol. 41, No. 15, pp. 3501–3527, 2003. [29] T. Jefferson, M. Rangaswami, and G. Stoner, “Simulation in the Design of Ground-based Intrabay Automation Systems,” Winter Simulation Conference, Coronado, California, pp. 1008-1013, December 1996. [30] M. D. Jeng, C. Shilin, and Y. S. Huang, “Petri Net Dynamics-Based Scheduling of Flexible Manufacturing Systems with Assembly,” Journal of Intelligent Manufacturing, Vol. 10, No. 6, pp. 541-555, December 1999. [31] K. C. Jeong, and Y. D. Kim, “A Real-Time Scheduling Mechanism for a Flexible Manufacturing System: Using Simulation and Dispatching Rules,” International Journal of Production Research, Vol. 36, No. 9, pp. 2609-2626, 1998. [32] L. P. Khoo, S. G. Lee, and X. F. Yin, “A Prototype Genetic Algorithm-Enhanced Multi-Objective Scheduler for Manufacturing Systems,” International Journal of Advanced Manufacturing Technology, Vol. 16, No. 2, pp. 131-138, 2000. [33] C. O. Kim, H. S. Min, and Y. Yih, “Integration of Inductive Learning and Neural Networks for Multi-Objective FMS Scheduling,” International Journal of Production Research, Vol. 36, No. 9, pp. 2497-2509, 1998. [34] W. Kohn, V. Brayman, and J. Littleton, “Repair-Control of Enterprise Systems Using RFID sensory data,” IIE Transactions, Vol. 37, No. 4, pp. 281-290, April 2005. [35] C.H. Kuo, “Development of Distributed Component based Manufacturing System Framework,” Doctoral Dissertation, Institute of Mechanical Engineering, National Taiwan University, 1999. [36] R. Kurosaki, N. Nagao, H. Komada, Y. Watanabe, and H. Yano, “AMHS for 300 mm Wafer”, IEEE International Symposium on Semiconductor Manufacturing Conference, pp. 13-16, 1997. [37] A. Langevin, D. Lauzon, and D. Riopel, “Dispatching, Routing, and Scheduling of Two Automated Guided Vehicles in a FMS,” International Journal of Flexible Manufacturing Systems, Vol. 8, pp. 247–262, 1996. [38] J. Leea, and O. Korbaab, “Modeling and Scheduling of Ratio-Driven FMS Using Unfolding Time Petri Nets,” Computers & Industrial Engineering, Vol. 46, pp. 639–653, July 2004. [39] Y. S. Lin, “Development of an Intelligent Scheduling System for a Semiconductor Foundry Fab,” Master Thesis, Graduate Institute of Industrial Engineering, National Taiwan University, 2004. [40] Y. H. Liu, H. P. Huang, and Y. S. Lin, “Attribute Selection for the Scheduling of Flexible Manufacturing Systems based on Fuzzy Set-Theoretic Approach and Genetic Algorithm,” Journal of Chinese Institute of Industrial Engineers, Vol. 22, No. 1, pp. 46-55, 2005. [41] J. Liu, and B. L. MacCarthy, “General Heuristic Procedures and Solution Strategies for FMS Scheduling,” International Journal of Production Research, Vol. 37, No. 14, pp. 3305-3333, 1999. [42] A. D. Luca, and S. Termini, “A Definition of non Probabilistic Entropy in the Setting of Fuzzy Set Theory,” Information and Control, Vol. 20, pp. 301-312, 1972. [43] G. T. Mackulak, and F. P. Lawrence, “Semiconductor Fab Automation design & Application of Automated Material Handling Systems”, SEMI, pp. 6-41, 1998. [44] D. McFarlanea, S. Sarmab, J.L. Chirna, C. Y. Wonga, and K. Ashtonb, “Auto ID Systems and Intelligent Manufacturing Control,” Engineering Applications of Artificial Intelligence, Vol.16, Issue: 4, pp.365-376, 2003. [45] H. S. Min, “Development of a Real-time Multi-objective Scheduler for Semiconductor Manufacturing Plants,” Doctoral Dissertation, Purdue University, July, 2002. [46] H. S. Min, Y. Yih, and C. O. Kim, “A Competitive Neural Network Approach to Multi-Objective FMS Scheduling,” International Journal of Production Research, Vol. 36, No. 7, pp. 1749-1765, 1998. [47] M. Monkazeri, and L. V. Wassenhove, “Analysis of Scheduling Rules for an FMS,” International Journal of Production Research, Vol. 24, No. 4, pp. 785-802, 1990. [48] M. Norman, D. Tinsley, J. Barksdale, O. Wiersholm, P. Campbell, and E. Macnair, “Process and Material Handling Models Integration,” Proceedings of the 1999 Winter Simulation Conference, pp. 1262-1267, 1999. [49] H. Pierreval, and N. Mebarki, “Dynamic Scheduling Selection of Dispatching Rules for Manufacturing System,” International Journal of Production Research, Vol. 35, No. 6, pp. 1575-1591, 1997. [50] S. Piramuthu, M. Shaw, and B. Fulkerson, “Information-Based Dynamic Manufacturing System Scheduling,” International Journal of Flexible Manufacturing Systems, Vol. 12, No. 2, pp. 219-234, 2000. [51] J. C. Platt, N. Cristianini, and J. Shawe-Taylor, “Large Margin DAG’s for Multiclass Classification,” Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, Vol. 12, pp. 547–553, 2000. [52] S. G. Ponnambalam, P. Aravindan, and S. V. Rajesh, “A Tabu Search Algorithm for Job Shop Scheduling,” International Journal of Advanced Manufacturing Technology, Vol. 16, No. 10, pp. 765-771, 2000. [53] K. R. Rau, and O. V. K. Chetty, “Production Planning of FMS under Tool Magazine Constraints: a Dynamic Programming Approach,” International Journal of Advanced Manufacturing Technology, Vol. 11, pp. 366–371, 1996. [54] A. Reyes, H. Yu, G. Kelleher, and S. Lloyd, “Integrating Petri Nets and Hybrid Heuristic Search for the Scheduling of FMS,” Computers in Industry, Vol. 47, pp. 123-138, January 2002. [55] O. Rose, S. Chick, P. J. Sanchez, D. Ferrin, and D. J. Morrice, “Accelerating Products under Due-Date Oriented Dispatching Rules in Semiconductor Manufacturing,” Proceedings of the 2003 WinterSimulation Conference, pp. 1346–1350, 2003. [56] A. Rossi, and G. Dini, “Dynamic Scheduling of FMS Using a Real-Time Generic Algorithm,” International Journal of Production Research, Vol. 38, No. 1, pp. 1-20, 2000. [57] I. Sabuncuoglu, “A Study of Scheduling Rules of Flexible Manufacturing Systems: a Simulation Approach,” International Journal of Production Research, Vol. 36, No. 2, pp. 527-546, 1998. [58] M. Schulz, T. D. Stanley, and B. Renelt, “Simulation based Decision Support for Future 300 mm Automated Material Handling,” Proceedings of the 2000 Winter Simulation Conference, Piscataway, NJ., pp. 1518-1522, 2000. [59] R. W. Seifert, and S. Morito, “Cooperative dispatching-exploiting the flexibility of an FMS by means of incremental optimization,” European Journal of Operational Research, Vol. 129, No. 1, pp. 116-133, 2001. [60] S. Shimoyashiro, “Manufacturing System for LSI Wafer Fabrication,” Proceedings of the SEMI Technology Symposium, pp. 147-154, 1992. [61] O. P. Si, and S. F. Smith, “Towards an Opportunistic Scheduling System,” Nineteenth Annual Hawaii International Conference on System Sciences, Honolulu, 1986. [62] T. H. Soon, and R. D. Souza, “Intelligent Simulation-Based Scheduling of Workcells: an Approach,” Integrated Manufacturing Systems, Vol. 8, No. 1, pp. 6-23, 1997. [63] J. E. Spragg, and G. Kelleher, “A Discipline for Reactive Rescheduling,” AI in Planning and Scheduling, Ed. Austin Tate. AAAI Press, 1996. [64] C. H. Tsai, Y. M. Feng, and R. K. Li, “A hybrid dispatching rules in wafer fabrication factories,” International Journal of the Computer, the Internet and Management, Vol. 11, No. 1, pp. 64-72, April, 2003. [65] 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, pp. 121-139, March 2004. [66] C. C. Wang, “Modeling and Performance Evaluation for Automated Material Handling Systems in a 300mm Foundry Fab,” Master Thesis, Graduate Institute of Machine Engineering, National Taiwan University, 2001. [67] L. C. Wang, H. M. Chen, and C. M. Lin, “Intelligent Scheduling of FMS with Inductive Learning Capability Using Neural Networks,” International Journal of Flexible Manufacturing Systems, Vol. 7, No. 2, pp. 147-175, 1995. [68] L. M. Wein, “Scheduling Semiconductor Wafer Fabrication,” IEEE Transactions on Semiconductor Manufacturing, Vol. 1, No. 3, pp. 115-130, August 1988. [69] K. R. B. Xie, Reddy, N. Subramaniam, and Velusamy, “Dynamic Scheduling of Flexible Manufacturing Systems,” in Innovation in Manufacturing Systems and Technology (IMST), 2004. [70] M. Yamamoto, and S. Y. Nof, “Scheduling/Rescheduling in the Manufacturing Operating System Environment,” International Journal of Production Research, Vol. 23, No. 4, pp. 705-722, July/August 1985. [71] S. J. Yim, and D. Y. Lee, “Scheduling Cluster Tools in Wafer Fabrication Using Candidate List and Simulated Annealing,” Journal of Intelligent Manufacturing, Vol. 10, No. 6, pp. 531-540, 1999. [72] H. Yu, A. Reyes, S. Cang, and S. Lloyd, “Combined Petri Net Modeling and AI-based Heuristic Hybrid Search for Flexible Manufacturing Systems—Part II. Heuristic Hybrid Search,” Computers & Industrial Engineering, Vol. 44, pp. 545-566, 2003. [73] L. Yu, H. M. Shih, and T. Sekiguchi, “Fuzzy Inference-Based Multiple Criteria FMS Scheduling,” International Journal of Production Research, Vol. 37, No. 10, pp. 2315-2333, 1999. [74] M. C. Zhou, and M. D. Jeng, “Modeling, Analysis, Simulation, Scheduling, and Control of Semiconductor Manufacturing System: A petri Net Approach,” IEEE Transactions on Semiconductor Manufacturing, Vol. 11, No. 3, pp. 333–357, 1998. [75] eM-Plant Homepage: http://www.emplant.de/simulation.html [76] SEMATECH: http://www.sematech.org/ | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/24389 | - |
dc.description.abstract | 在半導體晶圓廠的生產規劃中,如何達到生產運作的全自動化,提高廠區設備的生產效率,是一極為重要的研究方向。從接到訂單時,依客戶重要性或訂單相關性,排單進入廠區生產;在訂單進入廠區之後,除了產品本身的加工時間,還要根據機台的使用率有不等的等候時間,最後在到期日前要交貨;所以排程是晶圓廠中的重要部分,可以有效控制時間,縮短生產週期及減少延遲個數。
本論文的主要目的在藉由RFID (Radio Frequency Identification)技術為基礎,提出一個排程和重新排程系統。此系統有三個主要的模組,分別為即時決策排程器(real-time decision-making scheduler)、考慮機台損壞和瓶頸機台的重新排程法則及彈性製造系統模擬平台(Simulation Platform of Flexible Manufacturing System, SPFMS)。 在決策排程器的建構上,我們利用RFID收集系統狀態,並以GA based FFEI (Genetic Algorithm based Fuzzy-entropy-based Feature Evaluation Index)選出在三個績效指標:產出量(Throughput)、平均流程時間(Mean Flow Time)、延遲交貨個數(Tardy Number)中顯著的屬性(salient attribute),再送進支向機(Support Vector Machine)做訓練,如此屬性可以找到與之相對應的派工法則,應用在適當的時間點做排程的依據。重新排程法則的提出則是為了因應晶圓廠內有不可預期失效值的發生,如機台損壞、瓶頸機台,而影響系統績效衡量;所以,我們考慮機台的平均無故障時間(Mean Time between Failure)和平均故障修復時間(Mean Time to Repair)造成的屬性變化,再將這些屬性送進決策排程器做重新排程,使得績效更佳。在模擬平台的實現上,我們利用模擬工具eM-Plant建構彈性製造系統模擬平台,並以合於國際標準的300mm晶圓廠和TRC fab為範例實做,來驗證我們所提出的排程法則跟重新排程法則。 最後,所實現的模擬平台與排程控制系統經驗證後,在績效指標的表現上,SEMATECH的結果單就平均流程時間(Mean Flow Time)比較,約減少3%(203.32->197.307小時);而延遲個數則平均減少9個(63->52個),約降低17%;平均產出量(Throughput)則增加了約0.6%(176852->177951 wafer)。TRC fab的結果則平均流程時間(Mean Flow Time)約減少4%(451.23->427.86小時);而延遲個數則平均減少3個(15->12個),約降低25%。 藉由GA based FFEI可以找出表現個別績效指標較好的顯著屬性,也可以選擇綜合三個績效指標的屬性,透過這樣的選擇再進行模擬後,此模擬結果證實,我們所提出的架構及方法達到了全自動化之生產運作的目標,不但線上收集資料迅速,於排程效能上也優於單一派工法則,更能有效解決機台損壞和瓶頸機台所影響的績效表現。 | zh_TW |
dc.description.abstract | In the fab planning, how to achieve a fully automated manufacturing to replace human operator in performing runtime scheduling and dispatching decisions is a very important issue. This thesis constructs a real-time scheduling and rescheduling system based on a new technology, RFID (Radio Frequency Identification), for fully automated fabs. The scheduling and rescheduling system consists of three modules: a real-time decision-making scheduler, a rescheduling mechanism for machine breakdowns and bottleneck, and a simulation platform for flexible manufacturing system (FMS).
In order to develop a real-time decision-making scheduler, we use RFID to collect system attributes and choose the salient attributes by GA based FFEI (Genetic Algorithm based Fuzzy-entropy-based Feature Evaluation Index). Then, the salient attributes corresponds to dispatching rules by using support vector machine (SVM). The proposed rescheduling framework is used when unknown machine breakdowns and bottleneck happen. We define the bottleneck tools with utilization of machines or queuing length, and then implement the rescheduling strategies. Next, we develop the simulation platform by using object-oriented software, eM-Plant. The implemented scheduling and rescheduling system have been demonstrated by two example manufacturing models, a 300mm factory fab of SEMATECH and TRC fab. In SEMATECH model, the mean flow time is reduced 3%(203.32->197.307 hours) and tardy numbers are decreased 17% (63->52 lots). We also have more throughput (176852->177951 wafer). In TRC fab model, the mean flow time is reduced 4% (451.23->427.86 hours) and tardy numbers are decreasing 25% (15->12 lots). The experimental results of simulation are better than previous researches. The dispatching rule sets proposed by SVM scheduler have higher performance than single dispatching rule. This scheduling and rescheduling framework can consider time of machine breakdowns and bottleneck and improve the system immediately. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T05:24:13Z (GMT). No. of bitstreams: 1 ntu-94-R92546006-1.pdf: 859147 bytes, checksum: 14357a0f9c675d28b0c5d7329d54079c (MD5) Previous issue date: 2005 | en |
dc.description.tableofcontents | 摘要 i
Abstract iii List of Tables vii List of Figures viii Chapter 1 Introduction 1 1.1. Motivations 1 1.2. Literature Survey 2 1.2.1. Fab Modeling 2 1.2.2. Scheduling Approaches 4 1.2.3. Rescheduling Strategies 7 1.3. Objectives 7 1.4. Contributions 8 1.5. Thesis Organization 8 Chapter 2 Background Knowledge 10 2.1. The RFID System 10 2.1.1. Components of the RFID System 10 2.1.2. Features of the RFID System 11 2.1.3. The Applications of the RFID System 12 2.2. Flexible Manufacturing Systems 12 2.3. IC Fabrication Manufacturing Systems 13 2.4. International Standards of Manufacturing Systems 16 2.5. Optimal Attributes Selection 17 2.6. The Simulation Software: eM-Plant 21 Chapter 3 Simulation Models 22 3.1. International SEMATECH Manufacturing Initiative (ISMI) 22 3.1.1. Model Specification 22 3.1.2. Model Verification 28 3.2. TRC Fab 31 3.2.1. Model Specification 31 3.2.2. Model Verification 33 Chapter 4 Hybrid Scheduling and Rescheduling Mechanism 36 4.1. Development Strategy of the Scheduler 36 4.1.1. Evaluation Criteria: System Attributes and Performance 38 4.1.2. Data Collection Using the RFID System 40 4.1.3. Experiment Results of Optimal Attributes Selection 44 4.2. Support Vector Machines 46 4.3. Rescheduling Mechanism 55 4.3.1. Definition 55 4.3.2. Architecture 56 4.3.3. Rescheduling Knowledge 56 4.4. Proposed Rescheduling Framework 57 4.4.1. Definition of Bottleneck 57 4.4.2. Rescheduling Strategies 58 Chapter 5 Experimental Results 60 5.1. Simulation Environment 60 5.1.1. Simulation Platform Preparation 60 5.1.2. Training Set Preparation 61 5.1.3. Testing Set Preparation 63 5.2. Experimental Results 63 5.2.1. Salient Attributes for Throughput 63 5.2.2. Salient Attributes for Mean Flow Time 64 5.2.3. Salient Attributes for Tardy Number 64 5.2.4. Salient Attributes for Three Performance Criteria 65 5.3. Experimental Results of Rescheduling 67 5.3.1. Experimental Results 67 Chapter 6 Conclusions and Future Works 69 6.1. Conclusions 69 6.2. Future Works 69 References 71 | |
dc.language.iso | en | |
dc.title | 以RFID為基的晶圓廠即時排程系統之發展 | zh_TW |
dc.title | Development of a Real-Time Scheduling and Rescheduling System based on RFID for Semiconductor Foundry Fabs | en |
dc.type | Thesis | |
dc.date.schoolyear | 93-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳正剛,劉益宏 | |
dc.subject.keyword | 晶圓廠,排程, | zh_TW |
dc.subject.keyword | RFID,Semiconductor Foundry Fabs,Scheduling,Rescheduling, | en |
dc.relation.page | 77 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2005-07-25 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-94-1.pdf 目前未授權公開取用 | 839.01 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。