Optimization and Control

  • Shimon Y. NofEmail author
  • Jose Ceroni
  • Wootae Jeong
  • Mohsen Moghaddam
Part of the Automation, Collaboration, & E-Services book series (ACES, volume 2)


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.


Swarm Intelligence Resource Agent Pheromone Trail Bullwhip Effect Event Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahn, H.J., Lee, H.: An Agent-based Dynamic Information Network for Supply Chain Management. BT Technology Journal 22(2), 18–27 (2004)CrossRefGoogle Scholar
  2. Akanle, O.M., Zhang, D.Z.: Agent-based model for optimizing supply-chain configurations. International Journal of Production Economics 115, 444–460 (2008)CrossRefGoogle Scholar
  3. Ayyash, A.A., Khatatneh, K.: HTTP Protocol Optimization. Working paper, Balqa Applied University (2012)Google Scholar
  4. 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
  5. Babanov, A., Collins, J., Gini, M.: Asking the right question: risk and expectation in multi agent contracting. AIEDAM 17(3), 173–186 (2003)CrossRefGoogle Scholar
  6. Baker, A.D.: Manufacturing control with a market-driven contract net. PhD Thesis, Rensselaer Polytechnic Institute, NY, USA (1991)Google Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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
  9. 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
  10. 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
  11. 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)zbMATHCrossRefGoogle Scholar
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. Chan, Y.S., Lee, J.K.: Case-based modification for optimization agents: AGENT-OPT’. DSS 36(4), 355–370 (2004)MathSciNetGoogle Scholar
  19. Choi, S.P.M., Liu, J., Chan, S.P.: A genetic agent-based negotiation system. Computer Networks 37, 195–204 (2001)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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
  22. 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
  23. 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)CrossRefGoogle Scholar
  24. Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. Bio Systems 43, 73–81 (1997)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. Esche, E., Müller, D., Kraus, R., Wozny, G.: Systematic approaches for model derivation for optimization purposes. Chemical Engineering Science (2013),
  27. 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)zbMATHCrossRefGoogle Scholar
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. Giret, A., Botti, V.: Holons and Agents. Journal of Intelligent Manufacturing 15, 645–659 (2004)CrossRefGoogle Scholar
  34. 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
  35. 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
  36. Hadeli, Valckenaers, P., Kollingbaum, M., Van Brusse, H.: Multi-agent coordination and control using stigmergy. Computers in Industry 53, 75–96 (2004)Google Scholar
  37. 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
  38. Hayes, C.C.: MAPP: An agent organization for process planning. Working Notes of the ABM Workshop, Minneapolis, MN, pp. 32–40 (1998)Google Scholar
  39. 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)zbMATHCrossRefGoogle Scholar
  40. Holl, S., Zimmermann, O., Palmblad, M., Mohammed, Y., Hofmann-Apitius, M.: A new optimization phase for scientific workflow management systems (2013),
  41. Hsieh, F.S., Chiang, C.Y.: Collaborative composition of processes in holonic manufacturing systems. Computers in Industry 62(1), 51–64 (2011)CrossRefGoogle Scholar
  42. 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
  43. Huang, C.Y., Nof, S.Y.: Formation of Autonomous Agent Networks for Manufacturing Systems. International Journal of Production Research 38(3), 607–624 (2000a)zbMATHCrossRefGoogle Scholar
  44. 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)CrossRefGoogle Scholar
  45. 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
  46. Jeong, W., Nof, S.Y.: Performance evaluation of wireless sensor network protocols for industrial applications. Journal of Intelligent Manufacturing 19, 335–345 (2008)CrossRefGoogle Scholar
  47. 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
  48. Kaihara, T.: Multi-agent based supply chain modelling with dynamic environment. International Journal of Production Economics 85(2), 263–269 (2003)CrossRefGoogle Scholar
  49. Karageorgos, A., Mehandjiev, N., Weichhart, G., Hammerle, A.: Agent-based optimisation of logistics and production planning. EAAI 16(4), 335–348 (2003)CrossRefGoogle Scholar
  50. 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)CrossRefGoogle Scholar
  51. Koestler, A.: The Ghost in the Machine. Hutchinson (Penguin Group), London (1967) ISBN 0-14-019192-5 (1990 reprint Ed.)Google Scholar
  52. Leitão, P.: Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence 22(7), 979–991 (2009)CrossRefGoogle Scholar
  53. 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
  54. 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)CrossRefGoogle Scholar
  55. Lin, G.Y.-J., Solberg, J.J.: Integrated shop floor control using autonomous agents. IIE Transactions: Design and Manufacturing 24(3), 57–71 (1992)CrossRefGoogle Scholar
  56. 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
  57. Lindstrom, P., Isenburg, M.: Fast and Efficient Compression of Floating-Point Data. IEEE Transactions on Visualization and Computer Graphics 12(5) (2006)Google Scholar
  58. 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)zbMATHCrossRefGoogle Scholar
  59. Marik, V., McFarlane, D.: Industrial adoption of agent-based technologies. IEEE Intelligent Systems 20(1), 27–35 (2005)CrossRefGoogle Scholar
  60. Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26, 369–395 (2004)zbMATHMathSciNetCrossRefGoogle Scholar
  61. 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)zbMATHCrossRefGoogle Scholar
  62. 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
  63. 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
  64. Miyashita, K.: CAMPS: A constraint-based architecture for multi agent planning and scheduling. Journal of Intelligent Manufacturing 9(2), 147–154 (1998)CrossRefGoogle Scholar
  65. 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)CrossRefGoogle Scholar
  66. 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
  67. NIST, Advanced Technology Program (1998),
  68. Nof, S.Y.: Intelligent, Collaborative Agents. In: Yearbook 2000, McGraw-Hill Encyclopedia of Science and Technology (2000)Google Scholar
  69. Pan, J.Y.C., Tenenbaum, M.J.: An intelligent agent framework for enterprise integration. IEEE TSMC 21(6), 1391–1408 (1991)Google Scholar
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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)CrossRefGoogle Scholar
  75. 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)CrossRefGoogle Scholar
  76. Rabelo, R.J.: Interoperating standards in multi-agent agile manufacturing scheduling systems. IJCAT 18(1-4), 146–159 (2003)CrossRefGoogle Scholar
  77. 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)CrossRefGoogle Scholar
  78. 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
  79. 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)CrossRefGoogle Scholar
  80. 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
  81. 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)CrossRefGoogle Scholar
  82. 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)CrossRefGoogle Scholar
  83. Sycara, K.P., Roth, S.F., Sadeh, N., Fox, M.S.: Resource allocation in distributed factory scheduling. IEEE Expert 6(1), 29–40 (1991)CrossRefGoogle Scholar
  84. Trentesaux, D.: Distributed control of production systems. Engineering Applications of Artificial Intelligence 22, 971–978 (2009)CrossRefGoogle Scholar
  85. Van Dyke Parunak, H.: “Go to the ant”: Engineering principles from natural multi-agent systems. Annals of Operations Research 75, 69–101 (1997)zbMATHCrossRefGoogle Scholar
  86. Van Leeuwen, E.H., Norrie, D.H.: Intelligent manufacturing: holons and holarchies. Manufacturing Engineer 76(2), 86–88 (1997)CrossRefGoogle Scholar
  87. 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)CrossRefGoogle Scholar
  88. 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)CrossRefGoogle Scholar
  89. 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)CrossRefGoogle Scholar
  90. 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)CrossRefGoogle Scholar
  91. 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)zbMATHCrossRefGoogle Scholar
  92. 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
  93. Wooldridge, M., Jennings, N.: Intelligent agents: Theory and practice. Knowledge Engineering Review 10(2), 115–152 (1995)CrossRefGoogle Scholar
  94. Yen, B.P.C., Wu, O.Q.: Internet scheduling environment with market driven agents. IEEE TSMC-A 34(2), 281–289 (2003)Google Scholar
  95. 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)CrossRefGoogle Scholar
  96. 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)CrossRefGoogle Scholar
  97. Yu, R., Iung, B., Panetto, H.: A multi-agent-based E-maintenance system with case-based reasoning decision support. EAAI 16, 321–333 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Shimon Y. Nof
    • 1
    Email author
  • Jose Ceroni
    • 2
  • Wootae Jeong
    • 3
  • Mohsen Moghaddam
    • 4
  1. 1.PRISM Center & School of IEPurdue University West LafayetteUSA
  2. 2.School of Industrial Engineering Catholic University of ValparaísoValparaísoChile
  3. 3.Korea Railroad Research Institute UiwangRepublic of South Korea
  4. 4.PRISM Center & School of IE Purdue UniversityWest LafayetteUSA

Personalised recommendations