以即時資料為基礎的作業現場製程規劃與彈性管控系統

A Field Work Planning and Flexible Control System Based on Real-Time Data

10.6840/cycu201700451

張彥文

網實整合系統 ； 生產排程 ； 多重代理人 ； Cyber-Physical System ； Production Scheduling ； Multi-Agent System

中原大學資訊管理學系學位論文

2017年

碩士

劉士豪

繁體中文

經濟全球化的現象迫使製造業必須以更具彈性的方式適應市場需求，但現行傳統製造業在進行生產時仍然仰賴人工作業安排生產計劃的排程，這種方式不僅勞心勞力也容易因為作業現場與生產管理人員之間的資訊落差導致反應能力不佳。因此本研究希望透過多重代理人技術協助安排傳統製造產業工廠內的生產排程計劃，利用代理人所具備的自主性與協調能力簡化繁複的排程作業，提高傳統工廠在生產製造上的表現及減少生產管理人員耗費在安排生產計劃上的時間與心力。代理人將以作業現場的即時資料為基礎進行自動化的協調，安排工單的投產、加工排序，使加工過程具批次生產的效益，並降低傳統製造產業對人工作業安排生產計劃的依賴，且減輕生產管理人員的負荷。 本次研究將利用Plant Simulation軟體作為系統模擬運行的實施方式，透過Plant Simulation建構模擬個案工廠內的機台狀態，並透過總完工時間(Total completion flowtime)、機台稼動率(Availability)、延遲時間(Tardiness)三項績效衡量指標評估本次研究中，以代理人進行作業現場製程自動化規劃、管控的表現。在代理人模式下的模擬結果發現，總完工時間與機台稼動率的表現較差，而延遲時間的表現則較好。

The phenomenon of global economic force manufacturing adapt itself to market demand in a more flexible way. However, the current traditional manufacturing industry still arrange production schedule by manpower. In this way, not only hard working but easily causes poor response ability between work site and production manager because information divide. This research expects traditional manufactory planning production schedule by multi-agent technology, which autonomy and coordination can simplify complex scheduling, reduce the time and effort of planning production schedule by production manager also improve the performance in manufacturing. Based on real-time data in site work, multi-agent will coordinate automatically to arrange the production and processing sorting of work orders. Then make process having the benefits of batch production, reduce the dependence on production plan in traditional manufacturing and decline production manager's load. This research will use the software, Plant Simulation, to be the system simulation, through this software to construct the simulation case factory’s machine status. Then, with three performances indicator, Total completion flowtime, Availability and Tardiness, can assess multi-agent carrying out process automation planning and the control of work site in this research. In the multi-agent mode, we found that it has poor performance on total completion time and availability, but better performance on tardiness.

1. Asadzadeh, L., & Zamanifar, K. (2010). An agent-based parallel approach for the job shop scheduling problem with genetic algorithms. Mathematical and Computer Modelling, 52(11–12), 1957–1965. https://doi.org/10.1016/j.mcm.2010.04.019
   連結：
2. Azadeh, A., Negahban, A., & Moghaddam, M. (2012). A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems. International Journal of Production Research, 50(2), 551–566. https://doi.org/10.1080/00207543.2010.539281
   連結：
3. Chen, M. (2012). Machine-to-Machine Communications: Architectures, Standards and Applications. KSII Transactions on Internet and Information Systems. https://doi.org/10.3837/tiis.2012.02.002
   連結：
4. Elmi, A., Solimanpur, M., Topaloglu, S., & Elmi, A. (2011). A simulated annealing algorithm for the job shop cell scheduling problem with intercellular moves and reentrant parts. Computers & Industrial Engineering, 61(1), 171–178. https://doi.org/10.1016/j.cie.2011.03.007
   連結：
5. Haupt, R. (1989). A survey of priority rule-based scheduling. Operations-Research-Spektrum, 11(1), 3–16. https://doi.org/10.1007/BF01721162
   連結：
6. Holthaus, O., & Rajendran, C. (1997). Efficient dispatching rules for scheduling in a job shop. International Journal of Production Economics, 48(1), 87–105. https://doi.org/10.1016/S0925-5273(96)00068-0
   連結：
7. Leitão, P., Colombo, A. W., & Karnouskos, S. (2016). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11–25. https://doi.org/10.1016/j.compind.2015.08.004
   連結：
8. Leitão, P., Rodrigues, N., Turrin, C., & Pagani, A. (2015). Multiagent System Integrating Process and Quality Control in a Factory Producing Laundry Washing Machines. IEEE Transactions on Industrial Informatics, 11(4), 879–886. https://doi.org/10.1109/TII.2015.2431232
   連結：
9. Li, J., Pan, Q., & Liang, Y.-C. (2010). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 59(4), 647–662. https://doi.org/10.1016/j.cie.2010.07.014
   連結：
10. Monostori, L. (2014). Cyber-physical Production Systems: Roots, Expectations and R&D Challenges. Procedia CIRP, 17, 9–13. https://doi.org/10.1016/j.procir.2014.03.115
    連結：
11. Rajendran, C., & Holthaus, O. (1999). A comparative study of dispatching rules in dynamic flowshops and jobshops. European Journal of Operational Research, 116(1), 156–170. https://doi.org/10.1016/S0377-2217(98)00023-X
    連結：
12. Renna, P. (2011). Multi-agent based scheduling in manufacturing cells in a dynamic environment. International Journal of Production Research, 49(5), 1285–1301. https://doi.org/10.1080/00207543.2010.518736
    連結：
13. Scholz-Reiter, B., Hildebrandt, T., & Tan, Y. (2013). Effective and efficient scheduling of dynamic job shops—Combining the shifting bottleneck procedure with variable neighbourhood search. CIRP Annals - Manufacturing Technology, 62(1), 423–426. https://doi.org/10.1016/j.cirp.2013.03.047
    連結：
14. Zhou, K., Liu, T., & Zhou, L. (2015). Industry 4.0: Towards future industrial opportunities and challenges. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) (pp. 2147–2152). https://doi.org/10.1109/FSKD.2015.7382284
    連結：
15. 周碩彥, & Dewabharata A. (2016). 智慧工廠簡介. 科儀新知, (208), 5–15.
    連結：
16. 熊詩敏. (2007). 結合優勢性質與基因遺傳演算法於具有整備時間之單機與非等效平行機台之研究 (thesis). 元智大學.
    連結：
17. 王怡惠. (2015). 從工業4.0看我國生產力4.0之挑戰. 臺灣經濟研究月刊, 38(8), 111–119.
    連結：
18. 盧銘勳, & 郭政宜. (2008). 應用螞蟻演算法於動態排程系統. 臺北科技大學學報, 41(2), 62–82.
    連結：
19. 許國屏. (2014). 應用自主性代理人機制於多階非相關平行機臺之研究 (thesis). 國立屏東科技大學.
    連結：
20. 許天淳. (2008). 排程數據之分析. Journal of Data Analysis, 3(3), 47–62.
    連結：
21. 黃文品. (2007). 開發混合式巨集啟發式方法求解具順序相依整備時間之非等效平行機台排程問題 (thesis). 國立政治大學. Retrieved from http://nccur.lib.nccu.edu.tw/handle/140.119/35286
    連結：
22. Baheti, R., & Gill, H. (2011). Cyber-physical systems. The Impact of Control Technology, 12, 161–166.
23. Conway, R. W., Maxwell, W. L., & Miller, L. W. (2012). Theory of Scheduling. Courier Corporation.
24. Foundation for Intelligent Physical Agents. (2002, December 6). FIPA Contract Net Interaction Protocol Specification. Retrieved from http://www.fipa.org/specs/fipa00029/SC00029H.html
25. Foundation for Intelligent Physical Agents. (2004, March 18). FIPA Agent Management Specification. Retrieved from http://www.fipa.org/specs/fipa00023/SC00023K.html
26. Hildebrandt, T., Heger, J., & Scholz-Reiter, B. (2010). Towards Improved Dispatching Rules for Complex Shop Floor Scenarios: A Genetic Programming Approach. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (pp. 257–264). New York, NY, USA: ACM. https://doi.org/10.1145/1830483.1830530
27. Khosravi, B., Bennell, J. A., & Potts, C. N. (2012). Train Scheduling and Rescheduling in the UK with a Modified Shifting Bottleneck Procedure. In D. Delling & L. Liberti (Eds.), 12th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (Vol. 25, pp. 120–131). Dagstuhl, Germany: Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik. https://doi.org/http://dx.doi.org/10.4230/OASIcs.ATMOS.2012.120
28. Knabe, T., Schillo, M., & Fischer, K. (2002). Improvements to the FIPA Contract Net Protocol for Performance Increase and Cascading Applications. In In International Workshop for Multi-Agent Interoperability at the German Conference on AI (KI-2002.
29. Lucke, D., Constantinescu, C., & Westkämper, E. (2008). Smart Factory - A Step towards the Next Generation of Manufacturing. In Manufacturing Systems and Technologies for the New Frontier (pp. 115–118). Springer, London. Retrieved from http://link.springer.com/chapter/10.1007/978-1-84800-267-8_23
30. Monostori, L., Váncza, J., & Kumara, S. R. T. (2006). Agent-Based Systems for Manufacturing. CIRP Annals - Manufacturing Technology, 55(2), 697–720. https://doi.org/10.1016/j.cirp.2006.10.004
31. Niemueller, T., Ewert, D., Reuter, S., Ferrein, A., Jeschke, S., & Lakemeyer, G. (2013). RoboCup Logistics League Sponsored by Festo: A Competitive Factory Automation Testbed. In RoboCup 2013: Robot World Cup XVII (pp. 336–347). Springer, Berlin, Heidelberg. Retrieved from http://link.springer.com/chapter/10.1007/978-3-662-44468-9_30
32. Rajkumar, R. (Raj), Lee, I., Sha, L., & Stankovic, J. (2010). Cyber-physical Systems: The Next Computing Revolution. In Proceedings of the 47th Design Automation Conference (pp. 731–736). New York, NY, USA: ACM. https://doi.org/10.1145/1837274.1837461
33. Russell, S. J., Norvig, P., Canny, J. F., Malik, J. M., & Edwards, D. D. (1995). Artificial intelligence: a modern approach (1st ed.). Prentice hall Upper Saddle River.
34. Twardowski, B., & Ryzko, D. (2015). IoT and Context-Aware Mobile Recommendations Using Multi-agent Systems. In 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Vol. 1, pp. 33–40). https://doi.org/10.1109/WI-IAT.2015.120
35. Vogel-Heuser, B., Diedrich, C., Pantförder, D., & Göhner, P. (2014). Coupling heterogeneous production systems by a multi-agent based cyber-physical production system. In 2014 12th IEEE International Conference on Industrial Informatics (INDIN) (pp. 713–719). https://doi.org/10.1109/INDIN.2014.6945601
36. Wang, Y.-C., & Usher, J. M. (2004). Learning policies for single machine job dispatching. Robotics and Computer-Integrated Manufacturing, 20(6), 553–562. https://doi.org/10.1016/j.rcim.2004.07.003
37. Zhang, Y., Huang, G. Q., Qu, T., & Ho, O. (2010). Agent-based workflow management for RFID-enabled real-time reconfigurable manufacturing. International Journal of Computer Integrated Manufacturing, 23(2), 101–112. https://doi.org/10.1080/09511920903440354
38. 經濟部技術處. (2015, June 4). 生產力4.0核心技術研發策略.
39. 陳禎祥, 高嘉和, 陳俊傑, 劉伯晟, & 林文賓. (2003). 迴流雙機流程工廠之動態排程探討. 遠東學報, 20(1), 191–210.
40. 高心婷. (2006). 動態零工型工廠派工法則模擬研究 (thesis). 朝陽科技大學. Retrieved from http://ir.lib.cyut.edu.tw:8080/handle/310901800/24010