臺灣博碩士論文加值系統

隨著產業全球化的趨勢，今日的製造環境所重視的是如何提升顧客服務水準（例如：縮短接單到出貨時間及降低存貨成本等）。然而，目前所使用的製造規劃與控制系統所提供的功能僅侷限於製造廠，當擴及到整體供應鏈時，往往需要其他的系統整合技術。無線射頻識別系統的應用，帶來即時性的資訊，藉由主動獲得這些即時性的資訊，可以有效提升整體供應鏈績效。即使無線射頻技術可帶來即時性的資訊，後端如果沒有一套健全的應用系統，將無法使這些資訊獲得最有效的應用。因此，本篇論文的目的為：（1）應用無線射頻技術與多重代理人系統，提出一個可以快速且動態的回應企業外部與內部變動的製造規劃與控制系統（agent-based manufacturing planning and control system;AMPCS）之系統架構；（2）發展AMPCS之系統分析與設計方法；（3）以東海大學自動化實驗室為環境，實做並驗證此系統架構。

In today’s manufacturing enterprise, the performance of customer service level (e.g., short ordering-to-delivery time, low price) is highly dependent on the effectiveness of its manufacturing planning and control system (MPCS). However, the function of today’s manufacturing planning and control is limited inside a manufacturing system and cannot effectively enhance the performance objectives (e.g., customer service level) in a supply chain environment which usually includes several components. Currently, RFID allows the accurate and detailed information of products to be followed in real time across the supply chain. However, RFID technique cannot support a rapid decision-making in a distributed and heterogeneous manufacturing environment. On the contrary, a multi-agent approach may be applied in a distributed and autonomous system which allows negotiation-based decision making. Although MAS can be employed in distributed and dynamic environment, it can not make the correct decision without the real-time information.
To cope with these requirements, it is necessary to develop a manufacturing planning and control system (MPCS) which employs the RFID technique and multi-agent system (MAS) to quickly and dynamically respond to the external and internal environment changes. Therefore, the objective of this research is to introduce an agent-based manufacturing planning and control system (AMPCS) framework and develop a system analysis and design method for an agent-based MPCS. In order to develop AMPCS, an agent-based MPCS in an automated manufacturing cell (AMC) in the Automation Laboratory of Tunghai University is implemented.

Abstract iii
TABLE OF CONTENTS v
LIST OF FIGURES vi
LIST OF TABLES viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 1
1.3 Research Objectives 3
1.4 Outline of the Thesis 3
Chapter 2 Literature Review 4
2.1 Manufacturing Planning and Control System (MPCS) 4
2.2 Multi-Agent System (MAS) 6
2.3 RFID system 7
Chapter 3 The System Framework and Development Method of AMPCS 10
3.1 The System Framework of AMPCS 10
3.2 System Development Method of AMPCS 12
3.2.1 The MaSE methodology 12
3.2.2 The KQML 14
3.2.3 BDI 15
3.2.4 The system develop method of AMPCS 16
Chapter 4 The System Analysis and Design of AMPCS 17
4.1 System analysis phase of AMPCS 17
4.2 System design phase of AMPCS 25
4.3 The Communication Message of AMPCS 31
4.4 The architecture of agent in AMPCS 35
4.5 Case illistration 45
Chapter 5 Summary and Conclusion 48
5.1 Conclusion 48
5.2 Future Research 48
Reference 50

[1]Zhang, J.B., Zhang, D.H., Wong, M.M. & Ng, T.J. (2003). Design and Development of a Cost-effective Fault-tolerant Execution and Control System for Discrete Manufacturing. Emerging Technologies and Factory Automation, 269-275.
[2]Rangarajan, T.S., Vijaykumar, A. & Suraj, S.S. (2004). Stop getting Strangled by your Supply Chain, Forrester Research.
[3]Intermec. Supply Chain RFID: How It Works And Why It Pays. White paper.
[4]Junichi, Y., Eiji, A. & Tatsuo, A. (2005). Parts and packets unification radio frequency identification application for construction. Automation in Construction, 14, 477-490.
[5]Hiroyuki, H., Norishige, I., Yutetsu, F., Takeshi, S. & Hiroyoshi, I. (2003). Network Agents for Life Cycle Support of Mechanical Parts. Third international Symposium on Environmentally Conscious Design and Inverse Manufacturing, Tokyo, Japan.
[6]Kwangyeol, R., Youngjun, S. & Mooyoung, J. (2003). Modeling and specifications of dynamic agents in fractal manufacturing systems. Computers in Industry, 52, 161-182.
[7]Tao, X. (2000). Design and implement of a mobile agent structured migration mechanism. J. Software, 11, 7, 918-923.
[8]Rabelo, R.J., Camarinha-Matos, L.M. & Afsarmanesh,H. (1999). Multi-agent-based agile scheduling. Robotics and Autonomous System, 27, 15-28.
[9]Lim, M.K. & Zhang, D.Z. (2004). An integrated agent-based approach for responsive control of manufacturing resources. Computers & Industrial Engineering, 46, 221-232.
[10]Wang, S.L., Xia, H., Liu, F., Tao, G.B., & Zhang, Z. (2002). Agent-based modeling and mapping of a manufacturing system. Journal of Materials Processing Technology, 129, 518-523.
[11]Paulo, S. & Carlos, R. (1999). A distributed architecture and negotiation protocol for scheduling in manufacturing systems. Computers in Industry, 38, 103-113
[12]Shaw, C.F., Keith, A. S. & Kevin K. J. (2005). Manufacturing planning and predictive process model integration using software agents. Advanced Engineering Information, 19, 135-142.
[13]Shen W, Norrie D. Dynamic manufacturing scheduling using both functional and resource related agents. Integrated Computer-Aided Eng 2002;8(1):17–30.
[14]Supply Chain Council. (2003). Supply Chain Operations Reference Model. SCOR-Wizard 5.8, Computer Software.
[15]Jan, O. & Birger, R. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. International Transactions in Operational Research, Vol. 2, 29-43.
[16]Wooldridge, M. (1997). Agent-based software engineering. IEEE Proceeding an Software Engineering, 144(1), 26-37.
[17]O’Hare, G., Jennings, N.(Eds), Foundations of Distributed Artificial Intelligence, Wiley, New York. 1996.
[18]Scott, A.D., Mark, F.W. & Clint, N.S. (2001). Multi-agent Systems Engineering, International Journal of Software Engineering and Knowledge Engineering, Vol.11, No.3, 231-258
[19]Jiri, L., Olga, S., Vladimir, M. & Michal, P. (2001). Application of multi-agent approach in production planning and modeling. Engineering Application of Artificial Intelligence, 14, 369-376.
[20]Scott, A.M. (1999). KQML & FLBC: Constructing Agent Communication Languages, 32th Hawaii International Conference on System Science.
[21]Kinney, D., Geoegeff, M., and Rao, A. (1996). A Methodology and Modeling Technique for Systems of BDI Agents, Lecture Notes in Artificial Intelligence, Vol.1038
[22]Olhager, J. & Rapp, B. (1990). Modern Concepts for Computer-based Manufacturing Planning and Control Systems. In H.E. Bradley (Ed.) Operational Research, 565-576
[23]Bennet, T., Olhager, J. & Rapp, B. (1994). Computerization of Manufacturing Planning and Control System, Evaluation of Production Management Methods, Amsterdam: North-Kolland, 274-286.
[24]Maria, C., & Andrea, S. (2000). Multi-agent Systems in Production Planning and Control : An Application to the Scheduling of Mixed-model Assembly Lines, Internal Jounral Production Economics, 68, 29-42.
[25]Katina, M., & Luke, M. (2005). The Pros and Cons of RFID in Supply Chain Management, International Conference on Mobile Business.
[26]Lang, D.B., Oshims, M. (1998). Programming and Developing Java Mobile Agents with Aglets, Addison-Wesley, Reading, MA.