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研究生:徐世倫
研究生(外文):Shih-Lun Hsu
論文名稱:應用閉迴式排程法於多重代理人之製造規劃與控制系統
論文名稱(外文):Application of a Closed-Loop Scheduling Approach for Multi-agent Planning and Manufacturing Control System
指導教授:鄭辰仰鄭辰仰引用關係王立志王立志引用關係
指導教授(外文):Chen-Yang ChengLi-Chih Wang
口試委員:邱垂昱汪維揚
口試委員(外文):Chui-Yu ChiuWei-Yang Wang
學位類別:碩士
校院名稱:東海大學
系所名稱:工業工程與經營資訊學系
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:2010
畢業學年度:98
語文別:英文
論文頁數:94
中文關鍵詞:RFID多重代理人系統製造規劃與控制系統閉迴式排程法
外文關鍵詞:RFIDMulti-Agent SystemManufacturing Planning and ControlClosed-loop Scheduling Approach
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  • 被引用被引用:1
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  • 下載下載:12
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在今日競爭激烈的製造業中,伴隨著高度不確定性與複雜的生產環境,必須要藉由有效的製造規劃系統來提升客戶的服務水準 (例如:縮短接收顧客訂單到出貨時間與降低存貨成本等)。然而,目前製造業中普遍使用階層式的製造規劃與控制系統,階層式的缺點是結構僵化、設計變更不易與 缺乏彈性,相對而言;多重代理人的系統設計變更容易並藉由代理人之間的相互溝通做為制定決策的依據,因此,本研究採用代理人技術來建立製造規劃控制系統的模式與模擬為基礎的閉迴式排程法來決定訂單下發至現場的生產順序。現今無線射頻系統被廣泛應用於各種產業,能夠主動蒐集即時資訊,將此技術應用於製造現場,能夠將生產資訊即時性的回饋 (例如:原物料、在製品與完成品),因此,製造現場生產的變更能夠讓閉迴式模擬排程法動態的調整生產計劃。
本研究將採用閉迴式排程法應用於代理人為基礎的製造規劃與控制系統,此系統主要分成四個部分:排程代理人、作業管理代理人、行動物件代理人與資源代理人。為了產生生產計劃,資源代理人必須相互競爭所生產的物件藉由修正後的最短工時競標演算法。閉迴式排程法會決定訂單的投料時間,不斷的調整訂單生產順序,直到排程的結果達到交期變異最小為止。此外,當製造現場發生額外的干擾 (例如:機台故障與緊急抽差單),此代理人為基礎的製造規劃與控制系統能夠重新產生新的生產作業,避免訂單延遲,經由本論文驗證後,此排程法充分結合無線射頻技術於多重代理人系統,能夠克服製造現場的干擾並且達到最短完工時間的排程結果。

With the growing uncertainty and complexity in today’s manufacturing enterprises, 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, most of the current MPCS that employed the hierarchical planning approach may have some drawbacks, such as structural rigidity, difficulty in designing a control system, and lack of flexibility. A multi-agent approach may be applied in a distributed and autonomous system that allows negotiation-based decision-making. Hence, this paper considers modeling and simulation of an agile multi-agent planning and manufacturing control system (AMPCS) that uses a closed-loop feedback simulation (CLFS) for distributed order releasing sequences at the shop floor.
Currently, RFID (Radio Frequency IDentification) technology has been implemented to enhance the visibility, accountability, track ability, and traceability of a manufacturing system. With this technology in place, the RFID technique in real-time basis can utilize accurate and detailed manufacturing information (e.g., raw material, WIP, products in factory and products in the down streams) of products. Therefore, it responds to the dynamically changing manufacturing activities and exceptions. Application of a CLFS is intended to improve the performance of production schedule.
AMPCS includes four types of agents responsible for CLFS: scheduling agent (SA), job management agent (JMA), item mobile agent (IMA), and resource agent (RA). To generate the production schedule, RAs compete to win the IMA’s operation using the revised shortest processing time (SPT) bidding algorithm. The CLFS process is carried out based on the arrival time of JMA. The JMA submits SA the total processing time to compute the arrival time of each JMA. The RAs re-bid for operations based on the new set of JMA releasing priority until the due date deviation is minimized. In addition, production items and manufacturing resources attached with RFID tags may actively provide feedback on real-time production status to create a new bid. The development of the CLFS demonstrates that the integration of the RFID technique and multi-agent system (MAS) in an AMPCS can really possess the responsiveness and flexibility in a distributed and dynamic manufacturing system.

Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Scheduling Problems in Manufacturing 2
1.3 Research Objective 4
1.4 Research Approach 4
1.5 Constraint of This Research 5
1.6 Organization of This Thesis 5
Chapter 2 Literature Review 7
2.1 Manufacturing Planning and Control System (MPCS) 7
2.2 Distributed Control of Production Systems 9
2.3Multi-Agent System (MAS) 12
2.3.1 Agent-based Production Scheduling 13
2.3.2 Bidding-based Methods 14
2.3.3 Non-Bidding-based Methods 17
2.4 Closed-Loop Scheduling Approach 18
2.5 RFID-Based Manufacturing Control 20
Chapter 3 Introduction to CLFS in AMPCS 23
3.1 The System Framework of AMPCS 23
3.2 Closed-Loop Feedback Simulation 26
3.2.1 Arrival Time Control for Scheduling 26
3.2.2 SPT bidding procedure 27
3.3 Application of RFID Technology in CLFS 31
3.3.1 Machine Breakdown 33
3.3.2 Rush Order 36
Chapter 4 Numerical Evaluation of CLFS 39
4.1 Experiment1 - A FMC Scheduling Problem 39
4.2 Experiment2 - 24 Test Bed Scheduling Problems 43
4.3 Experiment3 – Manufacturing Control 44
4.3.1 Machine Breakdown 47
4.3.2 Execution Time Early or Delay 48
4.3.3 The Arrival of Rush Orders 49
Chapter 5 Conclusion and Future Research 51
5.1 Conclusion 51
5.2 Future Research 52
Reference 53
APPEDIX 55

Aissani, N., Beldjilali, B., & Trentesaux, D. (2009). Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach. Engineering Applications of Artificial Intelligence, 22(7), 1089-1103.
Allahverdi, A., Gupta, J. N. D., & Aldowaisan, T. (1999). A review of scheduling research involving setup considerations. Omega, 27(2), 219-239.
Alpay, Ş. (2007). Agent Based Dynamic Job Shop Simulation System. In New Trends in Applied Artificial Intelligence (pp. 364-373).
Bennet, T., Olhager, J., & Rapp, B. (1994). Computerization of Manufacturing Planning and Control System. Paper presented at the Evaluation of Production Management Methods.
Borangiu, T., Gilbert, P., Ivanescu, N.-A., & Rosu, A. (2009). An implementing framework for holonic manufacturing control with multiple robot-vision stations. Engineering Applications of Artificial Intelligence, 22(4-5), 505-521.
Desmukh, A., Benjaafar, S., Talavage, J., & Barash, M. (1993). Comparison of centralized and distributed control policies for manufacturing systems. Paper presented at the Proceedings of the 2nd Industrial Engineering Research Conference Proceedings.
Jain, A. K., & Elmaraghy, H. A. (1997). Production scheduling/rescheduling in flexible manufacturing. International Journal of Production Research, 35(1), 281-309.
Kim, Y. K., Park, K., & Ko, J. (2003). A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Computers & Operations Research, 30(8), 1151-1171.
Kumar, R., Tiwari, M., & Shankar, R. (2003). Scheduling of flexible manufacturing systems: an ant colony optimization approach. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 217(10), 1443-1453.
Lee, D. Y., & DiCesare, F. (1994). Scheduling Flexible Manufacturing Systems Using Petri Nets and Heuristic Search. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 10, 123.
Leung, C. W., Wong, T. N., Mak, K. L., & Fung, R. Y. K. (2006). Integrating process planning and scheduling by an agent-based ant colony system. Paper presented at the The 36th international conference on computers and industrial engineering
Leung, C. W., Wong, T. N., Mak, K. L., & Fung, R. Y. K. (2010). Integrated process planning and scheduling by an agent-based ant colony optimization. Computers & Industrial Engineering, 59(1), 166-180.
Lim, M. K., Zhang, Z., & Goh, W. T. (2009). An iterative agent bidding mechanism for responsive manufacturing. Engineering Applications of Artificial Intelligence, 22(7), 1068-1079.
MacCarthy, B. L., & Liu, J. (1993). A new classification scheme for flexible manufacturing systems. International Journal of Production Research, 31(2), 299 - 309.
Naidu, E. B., & Viswanadham, H. (1992). An expert system for real-time scheduling in flexible manufacturing systems. Information and Decision Technology, 18, 151-170.
Olhager, J., & Rapp, B. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. International Transactions in Operational Research, 2, 29-43.
Prabhu, V. V., & Duffie, N. A. (1995). Distributed simulation approach for enabling cooperation between entities in heterarchical manufacturing systems. Paper presented at the Modeling, Simulation, and Control Technologies for Manufacturing, Philadelphia, PA, USA.
Pujo, P., Broissin, N., & Ounnar, F. (2009). PROSIS: An isoarchic structure for HMS control. Engineering Applications of Artificial Intelligence, 22(7), 1034-1045.
Ryu, K., Son, Y., & Jung, M. (2003). Modeling and specifications of dynamic agents in fractal manufacturing systems. Computers in Industry, 52(2), 161-182.
Seilonen, I., Pirttioja, T., & Koskinen, K. (2009). Extending process automation systems with multi-agent techniques. Engineering Applications of Artificial Intelligence, 22(7), 1056-1067.
Trentesaux, D. (2009). Distributed control of production systems. Engineering Applications of Artificial Intelligence, 22(7), 971-978.
Wang, L. C., & Lin, S. K. (2009). A multi-agent based agile manufacturing planning and control system. Computers & Industrial Engineering, 57(2), 620-640.
Wang, M., Wang, H., Vogel, D., Kumar, K., & Chiu, D. K. W. (2009). Agent-based negotiation and decision making for dynamic supply chain formation. Engineering Applications of Artificial Intelligence, 22(7), 1046-1055.
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(1-3), 518-523.
Wong, T. N., Leung, C. W., Mak, K. L., & Fung, R. Y. K. (2006). Dynamic shopfloor scheduling in multi-agent manufacturing systems. Expert Systems with Applications, 31(3), 486-494.

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