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Optimization and Control

  • Chapter
Revolutionizing Collaboration through e-Work, e-Business, and e-Service

Part of the book series: Automation, Collaboration, & E-Services ((ACES,volume 2))

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Abstract

Considering the foundations, tools, and emerging discoveries of collaborative e-Work, as discussed in Chapters 1 and 2, it is realized that optimization and control are focused primarily on the core elements of e-Systems; agents, protocols, and workflows. In this chapter, we will show that these elements compose a solid framework for optimization and control of collaboration in emerging distributed e-Work systems. In order to be able to efficiently pass the benefits on to more complex constructs such as autonomous agents systems, production units configuration, highly reactive control protocols, and so on, these elements must be optimized as well. In order to show the evidence of the latest developments in optimization and control involving agent, protocol, and workflow theories, this chapter reviews the state-of-the-art techniques for achieving optimal design and operational control, and collaboration engineering. This chapter covers the incentives to construct autonomous agent-based systems, the key e-Criteria emerging from the transformation from traditional centralized work systems to decentralized e-Work systems, and several real-life applications of agent-based systems. Basic agent-based optimization and control architectures are reviewed along with pioneering bioinspired mechanisms based on swarm intelligence and natural evolution, and their impact on the intelligence and autonomy of agents. Several techniques for protocol and workflow optimization are also discussed.

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References

  • Ahn, H.J., Lee, H.: An Agent-based Dynamic Information Network for Supply Chain Management. BT Technology Journal 22(2), 18–27 (2004)

    Article  Google Scholar 

  • Akanle, O.M., Zhang, D.Z.: Agent-based model for optimizing supply-chain configurations. International Journal of Production Economics 115, 444–460 (2008)

    Article  Google Scholar 

  • Ayyash, A.A., Khatatneh, K.: HTTP Protocol Optimization. Working paper, Balqa Applied University (2012)

    Google Scholar 

  • Azevedo, A., Torscano, C., Sousa, J.P.: An order planning system to support networked supply chains. In: Proc. of PRO-VE 2002, pp. 237–244 (2002)

    Google Scholar 

  • Babanov, A., Collins, J., Gini, M.: Asking the right question: risk and expectation in multi agent contracting. AIEDAM 17(3), 173–186 (2003)

    Article  Google Scholar 

  • Baker, A.D.: Manufacturing control with a market-driven contract net. PhD Thesis, Rensselaer Polytechnic Institute, NY, USA (1991)

    Google Scholar 

  • Barbati, M., Bruno, G., Genovese, A.: Applications of agent-based models for optimization problems: A literature review. Expert Systems with Applications 39, 6020–6028 (2012)

    Article  Google Scholar 

  • Barber, S., White, E., Goel, A., Han, D., Kim, J., Li, H., Liu, T.H., Martin, C.E., McKay, R.: Sensible agent problem-solving simulation for manufacturing environments. In: Proc. of AI & Manufacturing Research Planning Workshop, Albuquerque, NM, pp. 1–9 (1998)

    Google Scholar 

  • Barry, J., Aparicio, M., Durniak, T., Herman, P., Karuturi, J., Woods, C., Gilman, C., Lam, H., Ramnath, R.: NIIIP-SMART: an investigation of distributed object approaches to support MES development and deployment in a virtual enterprise. In: Proc. of EDOC 1998, La Jolla, CA, pp. 366–377 (1998)

    Google Scholar 

  • Berry, N.M., Kumura, S.: Evaluating the design and development of Reagere. Working Notes of the ABM (Agent-Based Manufacturing) Workshop, Minneapolis, MN, pp. 5–13 (1998)

    Google Scholar 

  • Böcker, J., Lind, J., Zirkler, B.: Using a multi-agent approach to optimise the train coupling and sharing system. European Journal of Operational Research 131(2), 242–252 (2001)

    Article  MATH  Google Scholar 

  • Bremer, C.F., Molina, W.M.: Global virtual business – a systematic approach for exploiting business opportunities in dynamic markets. IJAM 1(12) (1999)

    Google Scholar 

  • Brennan, R., Balasubramanian, S., Norrie, D.H.: Dynamic control architecture for advanced manufacturing systems. In: Proc. of International Conference on Intelligent Systems for Advanced Manufacturing, Pittsburgh, PA, pp. 213–223 (1997)

    Google Scholar 

  • Bruckner, S., Wyns, J., Peeters, P., Kollingbaum, M.: Designing agents for the manufacturing process control. In: Proc. of AI and Manufacturing Research Planning Workshop, Albuquerque, NM, pp. 40–46 (1998)

    Google Scholar 

  • Budenske, J., Ahamad, A., Chartier, E.: Agent-based architecture for exchanging modeling data between applications. Working Notes of the ABM Workshop, Minneapolis, MN, pp. 18–27 (1998)

    Google Scholar 

  • Burke, P., Prosser, P.: The distributed asynchronous scheduler. In: Zweben, M., Fox, M.S. (eds.) Intelligent Scheduling, pp. 309–339. Morgan Kaufman Publishers, San Francisco (1994)

    Google Scholar 

  • Butler, J., Ohtsubo, H.: ADDYMS: Architecture for distributed dynamic manufacturing scheduling. In: Famili, A., Nau, D.S., Kim, S.H. (eds.) Artificial Intelligence Applications in Manufacturing, pp. 199–214. The AAAI Press (1992)

    Google Scholar 

  • Chan, Y.S., Lee, J.K.: Case-based modification for optimization agents: AGENT-OPT’. DSS 36(4), 355–370 (2004)

    MathSciNet  Google Scholar 

  • Choi, S.P.M., Liu, J., Chan, S.P.: A genetic agent-based negotiation system. Computer Networks 37, 195–204 (2001)

    Article  Google Scholar 

  • Colombo, A., Schoop, R., Neubert, R.: An agent-based intelligent control platform for industrial holonic manufacturing systems. IEEE Transactions on Industrial Electronics 53(1), 322–337 (2006)

    Article  Google Scholar 

  • Cong, J., Fan, Y., Han, G., Jiang, W., Zhang, Z.: Behavior and communication co-optimization for systems with sequential communication media. In: 43rd ACM/IEEEDesign Automation Conference, pp. 675–678 (2006)

    Google Scholar 

  • Cost, R.S., Finin, T., Labrou, Y., Luan, X., Peng, Y., Soboroff, I., Mayfield, J., Boughannam, A.: An agent-based infrastructure for enterprise integration. In: Proc. of ASA/MA 1999, Palm Springs, CA, pp. 219–233 (1999)

    Google Scholar 

  • Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 26(1), 29–41 (1996)

    Article  Google Scholar 

  • Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. Bio Systems 43, 73–81 (1997)

    Article  Google Scholar 

  • Elliman, D.G., Youssef, S.M.: A New Intelligent Agent-based Strategy for Constrained Multiple Destination Routing Problems. Computer Journal 47(6), 708–727 (2004)

    Article  Google Scholar 

  • Esche, E., Müller, D., Kraus, R., Wozny, G.: Systematic approaches for model derivation for optimization purposes. Chemical Engineering Science (2013), http://dx.doi.org/10.1016/j.ces.2013.11

  • Fischer, T., Gehring, H.: Planning vehicle transhipment in a seaport automobile terminal using a multi-agent system. European Journal of Operational Research 166(3), 726–740 (2005)

    Article  MATH  Google Scholar 

  • Floreano, D., Husbands, P., Nolfi, S.: Evolutionary Robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, vol. 61, pp. 1423–1451 (2008)

    Google Scholar 

  • Fordyce, K., Sullivan, G.G.: Logistics management system (LMS): integrating decision technologies for dispatch scheduling in semiconductor manufacturing. In: Zweben, M., Fox, M.S. (eds.) Intelligent Scheduling, pp. 473–516. Morgan Kaufman Publishers, San Francisco (1994)

    Google Scholar 

  • Fox, M.S.: Issues in Enterprise Modelling. In: Nof, S.Y. (ed.) Information and Collaboration Models of Integration, pp. 219–234. Kluwer Academic Publishers (1993)

    Google Scholar 

  • Fox, M.S., Chionglo, J.F., Barbuceanu, M.: The integrated supply chain management system. Internal Report, Dept. of Industrial Engineering, Univ. of Toronto (1993)

    Google Scholar 

  • Frey, D., Stockheim, T., Woelk, P.-O., Zimmermann, R.: Integrated multi-agent-based supply chain management. In: Proc. of 2003 WET ICE, Linz, Austria, pp. 24–29 (2003)

    Google Scholar 

  • Giret, A., Botti, V.: Holons and Agents. Journal of Intelligent Manufacturing 15, 645–659 (2004)

    Article  Google Scholar 

  • Goldsmith, S.Y., Interrante, L.D.: An autonomous manufacturing collective for job shop scheduling. In: Proc. of AI & Manufacturing Research Planning Workshop, Albuquerque, NM, pp. 69–74 (1998)

    Google Scholar 

  • Gupta, A., Whitman, L., Agarwal, K.: Supply chain agent decision aid system (SCADAS). In: Proc. of the 2001 Winter Simulation Conference, Arlington, VA, pp. 553–559 (2001)

    Google Scholar 

  • Hadeli, Valckenaers, P., Kollingbaum, M., Van Brusse, H.: Multi-agent coordination and control using stigmergy. Computers in Industry 53, 75–96 (2004)

    Google Scholar 

  • Hayden, M., van Renesse, R.: Optimizing layered communication protocols. In: Proceedings of the Sixth IEEE International Symposium on High Performance Distributed Computing, pp. 169–177 (1997)

    Google Scholar 

  • Hayes, C.C.: MAPP: An agent organization for process planning. Working Notes of the ABM Workshop, Minneapolis, MN, pp. 32–40 (1998)

    Google Scholar 

  • Ho, Y.C., Moodie, C.L.: Solving cell formation problems in a manufacturing environment with flexible processing and routing capabilities. International Journal of Production Research 34(10), 2901–2923 (1996)

    Article  MATH  Google Scholar 

  • Holl, S., Zimmermann, O., Palmblad, M., Mohammed, Y., Hofmann-Apitius, M.: A new optimization phase for scientific workflow management systems (2013), http://dx.doi.org/10.1016/j.future.2013.09.005

  • Hsieh, F.S., Chiang, C.Y.: Collaborative composition of processes in holonic manufacturing systems. Computers in Industry 62(1), 51–64 (2011)

    Article  Google Scholar 

  • Huang, C.Y., Chen, W.L., Huang, W.J.: Communication Protocols for Collaborative Production Planning. In: The 11th Asia Pacific Industrial Engineering and Management Systems Conference and the 14th Asia Pacific Regional Meeting of International Foundation for Production Research Melaka, December 7-10 (2010)

    Google Scholar 

  • Huang, C.Y., Nof, S.Y.: Formation of Autonomous Agent Networks for Manufacturing Systems. International Journal of Production Research 38(3), 607–624 (2000a)

    Article  MATH  Google Scholar 

  • Huang, C.Y., Nof, S.Y.: Autonomy and Viability – Measures for Agent-Based Manufacturing Systems. International Journal of Production Research 38(17), 4129–4148 (2000b)

    Article  Google Scholar 

  • Jennings, N.R., Sycara, K., Wooldridge, M.: A Roadmap of Agent Research and Development. Autonomous Agents and Multi-Agent Systems 1, 275–306 (1998)

    Google Scholar 

  • Jeong, W., Nof, S.Y.: Performance evaluation of wireless sensor network protocols for industrial applications. Journal of Intelligent Manufacturing 19, 335–345 (2008)

    Article  Google Scholar 

  • Jha, K.N., Morris, A., Mytych, E., Spering, J.: MADEsmart: Agents for design, analysis, and manufacturbility. Working Notes of the ABM Workshop, Minneapolis, MN, pp. 57–63 (1998)

    Google Scholar 

  • Kaihara, T.: Multi-agent based supply chain modelling with dynamic environment. International Journal of Production Economics 85(2), 263–269 (2003)

    Article  Google Scholar 

  • Karageorgos, A., Mehandjiev, N., Weichhart, G., Hammerle, A.: Agent-based optimisation of logistics and production planning. EAAI 16(4), 335–348 (2003)

    Article  Google Scholar 

  • Kimbrough, S.O., Wu, D.J., Zhong, F.: Computers play the beer game: can artificial agents manage supply chains? Decision Support Systems 33, 323–333 (2002)

    Article  Google Scholar 

  • Koestler, A.: The Ghost in the Machine. Hutchinson (Penguin Group), London (1967) ISBN 0-14-019192-5 (1990 reprint Ed.)

    Google Scholar 

  • Leitão, P.: Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence 22(7), 979–991 (2009)

    Article  Google Scholar 

  • Leue, S., Oechslin, P.: Optimization Techniques for Parallel Protocol Implementation. In: Proceedings of the Fourth Workshop on Future Trends of Distributed Computing Systems, pp. 387–393 (1993)

    Google Scholar 

  • Leung, C.W., Wong, T.N., Mak, K.L., Fung, R.Y.K.: Integrated process planning and scheduling by an agent-based ant colony optimization. Computers & Industrial Engineering 59, 166–180 (2010)

    Article  Google Scholar 

  • Lin, G.Y.-J., Solberg, J.J.: Integrated shop floor control using autonomous agents. IIE Transactions: Design and Manufacturing 24(3), 57–71 (1992)

    Article  Google Scholar 

  • Lin, F., Lin, S.: Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems. In: Proceedings of the Fourth Workshop on Knowledge Economy and Electronic Commerce, Taiwan (2006)

    Google Scholar 

  • Lindstrom, P., Isenburg, M.: Fast and Efficient Compression of Floating-Point Data. IEEE Transactions on Visualization and Computer Graphics 12(5) (2006)

    Google Scholar 

  • Liu, Y., Nof, S.Y.: Distributed micro flow sensor arrays and networks: Design architectures and communication protocols. International Journal of Production Research 42(15), 3101–3115 (2004)

    Article  MATH  Google Scholar 

  • Marik, V., McFarlane, D.: Industrial adoption of agent-based technologies. IEEE Intelligent Systems 20(1), 27–35 (2005)

    Article  Google Scholar 

  • Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26, 369–395 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  • Maturana, F., Shen, W., Norrie, D.H.: MetaMorph: an adaptive agent-based architecture for intelligent manufacturing. International Journal of Production Research 37(10), 2159–2174 (1999)

    Article  MATH  Google Scholar 

  • McEleney, B., O’Hare, G.M.P., Sampson, J.: An agent-based system for reducing changeover delays in a job-shop factory environment. In: Proc. of PAAM 1998, London, UK, pp. 591–613 (1998)

    Google Scholar 

  • Mehra, A., Nissen, M.: Intelligent supply chain agents using ADE. In: Proc. of AI & Manufacturing Research Planning Workshop, Albuquerque, NM, pp. 112–119 (1998)

    Google Scholar 

  • Miyashita, K.: CAMPS: A constraint-based architecture for multi agent planning and scheduling. Journal of Intelligent Manufacturing 9(2), 147–154 (1998)

    Article  Google Scholar 

  • Mönch, L., Stehli, M., Zimmermann, J.: FABMAS: An agent-based system for production control of semiconductor manufacturing processes. In: Mařík, V., McFarlane, D.C., Valckenaers, P. (eds.) HoloMAS 2003. LNCS (LNAI), vol. 2744, pp. 258–267. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  • Narzisi, G., Mysore, V., Mishra, B.: Multi-Objective Evolutionary Optimization of Agent-Based Models: An Application to Emergency Response Planning. In: Proceedings of Computational Intelligence Conference, USA (2006)

    Google Scholar 

  • NIST, Advanced Technology Program (1998), http://www.atp.nist.gov/

  • Nof, S.Y.: Intelligent, Collaborative Agents. In: Yearbook 2000, McGraw-Hill Encyclopedia of Science and Technology (2000)

    Google Scholar 

  • Pan, J.Y.C., Tenenbaum, M.J.: An intelligent agent framework for enterprise integration. IEEE TSMC 21(6), 1391–1408 (1991)

    Google Scholar 

  • Parunak, H.V.D.: MASCOT: A virtual factory for research and development in manufacturing scheduling and control. Technical Memo 93-02, Industrial Technology Institute (1993)

    Google Scholar 

  • Parunak, H.V.D., Baker, A., Clark, S.: The AARIA agent architecture: From manufacturing requirements to agent-based system design. Working Notes of the ABM Workshop, Minneapolis, MN, pp. 136–145 (1998)

    Google Scholar 

  • Pechoucek, M., Marik, V., Stepankova, O.: Coalition formation in manufacturing multi-agent systems. In: Proc. of DEXA 2000, London, UK, pp. 241–246 (2000)

    Google Scholar 

  • Peng, Y., Finin, T., Labrou, Y., Chu, B., Long, J., Tolone, W.J., Boughannam, A.: A multi-agent system for enterprise integration. In: Proc. of PAAM 1998, London, UK, pp. 155–169 (1998)

    Google Scholar 

  • Peralta, J., Annusornnitisarn, P., Nof, S.Y.: Analysis of a time-out protocol and its applications in a single server environment. International Journal of Computer Integrated Manufacturing 16(1), 1–13 (2003)

    Article  Google Scholar 

  • Putnik, G., Sluga, A., ElMaraghy, H., Teti, R., Koren, Y., Tolio, T., Hon, B.: Scalability in manufacturing systems design and operation: State-of-the-art and future developments roadmap. CIRP Annals - Manufacturing Technology 62, 751–774 (2013)

    Article  Google Scholar 

  • Rabelo, R.J.: Interoperating standards in multi-agent agile manufacturing scheduling systems. IJCAT 18(1-4), 146–159 (2003)

    Article  Google Scholar 

  • Reaidy, J., Massotte, P., Diep, D.: Comparison of negotiation protocols in dynamic agent-based manufacturing systems. International Journal of Production Economics 99(1-2), 117–130 (2006)

    Article  Google Scholar 

  • Rosenschein, J.S., Ephrati, E.: New approaches to multi-agent planning. In: Nof, S.Y. (ed.) Information and Collaboration Models of Integration, pp. 340–364. Kluwer Academic Publishers (1993)

    Google Scholar 

  • Ross, A., Rhodes, D., Hastings, D.: Defining Changeability: Reconciling Flexibility, Adaptability, Scalability, Modifiability, and Robustness for Maintaining System Lifecycle Value. Systems Engineering 11(3), 246–262 (2008)

    Article  Google Scholar 

  • Sacile, R., Paolucci, M., Boccalatte, A.: The MAKE-IT: Manufacturing agents in a knowledge-based environment driven by internet technologies. In: Proc. of the 2000 Academia/Industry Working Conference on Research Challenges, Buffalo, NY, pp. 281–291 (2000)

    Google Scholar 

  • Sadeh, N., Hildum, D.W., Kjenstad, D.: Agent-based e-supply chain decision support. Journal of Organizational Computing and Electronic Commerce 33(3-4), 225–241 (2003)

    Article  Google Scholar 

  • Shen, W., Maturana, F., Norrie, D.H.: MetaMorph II: an agent-based architecture for distributed intelligent design and manufacturing. Journal of Intelligent Manufacturing 11(3), 237–251 (2000)

    Article  Google Scholar 

  • Sycara, K.P., Roth, S.F., Sadeh, N., Fox, M.S.: Resource allocation in distributed factory scheduling. IEEE Expert 6(1), 29–40 (1991)

    Article  Google Scholar 

  • Trentesaux, D.: Distributed control of production systems. Engineering Applications of Artificial Intelligence 22, 971–978 (2009)

    Article  Google Scholar 

  • Van Dyke Parunak, H.: “Go to the ant”: Engineering principles from natural multi-agent systems. Annals of Operations Research 75, 69–101 (1997)

    Article  MATH  Google Scholar 

  • Van Leeuwen, E.H., Norrie, D.H.: Intelligent manufacturing: holons and holarchies. Manufacturing Engineer 76(2), 86–88 (1997)

    Article  Google Scholar 

  • Velásquez, J.D., Nof, S.Y.: A best-matching protocol for collaborative e-Work and e-Manufacturing. International Journal of Computer Integrated Manufacturing 21(8), 943–956 (2008)

    Article  Google Scholar 

  • Vrba, P., Tichy, P., Marık, V., Hall, K.H., Staron, R.J., Maturana, F.P., Kadera, P.: Rockwell Automation’s Holonic and Multi-agent Control Systems Compendium. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews 41(1), 14–30 (2011)

    Article  Google Scholar 

  • Wang, Y., Xiao, T., Duan, G., Wang, X.: Research on CAPP/scheduling integration multi-agent system model and implementation. CJME 16(4), 348–351 (2003)

    Article  Google Scholar 

  • Wiendahl, H.-P., ElMaraghy, H., Nyhuis, P., Zah, M.F., Wiendahl, H.-H., Duffie, N., Brieke, M.: Changeable Manufacturing – Classification, Design and Operation. CIRP Annals Manufacturing Technology 56(2), 783–809 (2007)

    Article  Google Scholar 

  • Williams, N.P., Liu, Y., Nof, S.Y.: The TestLAN approach and protocols for the integration of distributed assembly and test networks. International Journal of Production Research 40(17), 4505–4522 (2002)

    Article  MATH  Google Scholar 

  • Witten, I.H., Moffat, A., Bell, T.C.: Managing Gigabytes: Compressing and Indexing documents and images, 2nd edn., 519 p. Morgan Kaufmann (1999)

    Google Scholar 

  • Wooldridge, M., Jennings, N.: Intelligent agents: Theory and practice. Knowledge Engineering Review 10(2), 115–152 (1995)

    Article  Google Scholar 

  • Yen, B.P.C., Wu, O.Q.: Internet scheduling environment with market driven agents. IEEE TSMC-A 34(2), 281–289 (2003)

    Google Scholar 

  • Yoon, S.Y., Nof, S.Y.: Demand and capacity sharing decisions and protocols in a collaborative network of enterprises. Decision Support Systems 49(4), 442–450 (2010)

    Article  Google Scholar 

  • Yu, C.Y., Huang, H.P.: Development of the order fulfillment process in the foundry fab by applying distributed multi-agents on a generic message-passing platform. IEEE/ASME Transactions on Mechatronics 6(4), 387–398 (2001)

    Article  Google Scholar 

  • Yu, R., Iung, B., Panetto, H.: A multi-agent-based E-maintenance system with case-based reasoning decision support. EAAI 16, 321–333 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. PRISM Center & School of IE, Purdue University , West Lafayette, Indiana, USA

    Shimon Y. Nof

  2. School of Industrial Engineering , Catholic University of Valparaíso, Valparaíso, Chile

    Jose Ceroni

  3. Korea Railroad Research Institute , 437-757, Uiwang, Republic of South Korea

    Wootae Jeong

  4. PRISM Center & School of IE , Purdue University, West Lafayette, Indiana, USA

    Mohsen Moghaddam

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  1. Shimon Y. Nof
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  2. Jose Ceroni
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  4. Mohsen Moghaddam
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Nof, S.Y., Ceroni, J., Jeong, W., Moghaddam, M. (2015). Optimization and Control. In: Revolutionizing Collaboration through e-Work, e-Business, and e-Service. Automation, Collaboration, & E-Services, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45777-1_4

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