Exploring the Design Space for Myopia-Avoiding Distributed Control Systems Using a Classification Model

  • Tianyi Wang
  • Henning Blunck
  • Julia Bendul
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 694)


Avoiding myopia, suboptimal behaviour, caused by the limited information horizon and computation capacity of agents, has been recognized as a major design challenge for the future academic development and industrial adoption of distributed production control systems. In [3] existing literature from various research streams has been reviewed to classify design decisions that can be made to avoid myopic decision making. In the present paper, this model will be validated by mapping different paradigms of distributed control onto it. Through this exercise, an initial validation of the proposed classification model can be attained and a starting point for a classification of existing distributed production control approaches based on design features is provided. This will help designers of distributed architectures in production control to better understand their design space, take deliberate steps towards the avoidance of myopic behaviour, and identify unexplored areas within the design space.


Production control Myopia Distributed decision making Classification model 


  1. 1.
    Barbosa, J., Leitão, P., Adam, E., Trentesaux, D.: Dynamic self-organization in holonic multi-agent manufacturing systems: the adacor evolution. Comput. Ind. 66, 99–111 (2015)CrossRefGoogle Scholar
  2. 2.
    Blunck, H., Bendul, J.: Invariant-based production control reviewed: mixing hierarchical and heterarchical control in flexible job shop environments. In: Mařík, V., Schirrmann, A., Trentesaux, D., Vrba, P. (eds.) Industrial Applications of Holonic and Multi-Agent Systems, Lecture Notes in Computer Science, vol. 9266, pp. 96–107. Springer International Publishing (2015)Google Scholar
  3. 3.
    Blunck, H., Bendul, J.: Controlling myopic behavior in distributed production systems—a classification of design choices. Procedia CIRP 57, 158–163 (2016). Factories of the Future in the digital environment—Proceedings of the 49th CIRP Conference on Manufacturing SystemsGoogle Scholar
  4. 4.
    Botti, V., Giret, A.: ANEMONA—A Multi-Agent Methodology for Holonic Manufacturing Systems. Springer Series in Advanced Manufacturing, Springer, London (2008)Google Scholar
  5. 5.
    Cochran, J.K., Kaylani, H.A.: Optimal design of a hybrid push/pull serial manufacturing system with multiple part types. Int. J. Prod. Res. 46(4), 949–965 (2008)CrossRefzbMATHGoogle Scholar
  6. 6.
    Dilts, D., Boyd, N., Whorms, H.: The evolution of control architectures for automated manufacturing systems. J. Manuf. Syst. 10(1), 79–93 (1991)CrossRefGoogle Scholar
  7. 7.
    Farid, A., Ribeiro, L.: An axiomatic design of a multiagent reconfigurable mechatronic system architecture. Ind. Inf. IEEE Trans. 11(5), 1142–1155 (2015)Google Scholar
  8. 8.
    Giret, A., Trentesaux, D.: Software engineering methods for intelligent manufacturing systems: a comparative survey. In: Mařík, V., Schirrmann, A., Trentesaux, D., Vrba, P. (eds.) Industrial Applications of Holonic and Multi-Agent Systems, Lecture Notes in Computer Science, vol. 9266, p. 11–21. Springer International Publishing (2015)Google Scholar
  9. 9.
    Hatvany, J.: Intelligence and cooperation in heterarchic manufacturing systems. Robot. Comput. Integr. Manuf. 2(2), 101–104 (1985)CrossRefGoogle Scholar
  10. 10.
    Ho, J.C., Chang, Y.L.: An integrated MRP and JIT framework. Comput. Ind. Eng. 41(2), 173–185 (2001)CrossRefGoogle Scholar
  11. 11.
    Hopp, W.J., Spearman, M.L.: Factory Physics, vol. 3. McGraw-Hill/Irwin New York (2008)Google Scholar
  12. 12.
    Leitão, P., Restivo, F.: ADACOR: a holonic architecture for agile and adaptive manufacturing control. Comput. Ind. 57(2), 121–130 (2006)CrossRefGoogle Scholar
  13. 13.
    Malone, T.W., Crowston, K.: The interdisciplinary study of coordination. ACM Comput. Surv. 26(1), 87–119 (1994)CrossRefGoogle Scholar
  14. 14.
    Mařík, V., Lažanský, J.: Industrial applications of agent technologies. Control Eng. Pract. 15(11), 1364 – 1380 (2007). Special Issue on Manufacturing Plant Control: Challenges and Issues INCOM 2004 11th IFAC INCOM’04 Symposium on Information Control Problems in ManufacturingGoogle Scholar
  15. 15.
    Mařík, V., McFarlane, D.: Industrial adoption of agent-based technologies. IEEE Intell. Syst. 20(1), 27–35 (2005)CrossRefGoogle Scholar
  16. 16.
    McFarlane, D.C., Bussmann, S.: Holonic manufacturing control: rationales, developments and open issues, chap. 13, pp. 303–326. Springer, Berlin, Heidelberg (2003)Google Scholar
  17. 17.
    Philipp, T., Böse, F., Windt, K.: Evaluation of autonomously controlled logistic processes. In: Proceedings of 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, pp. 347–352. Ischia, Italy (2006)Google Scholar
  18. 18.
    Ryan, S.M., Baynat, B., Choobineh, F.F.: Determining inventory levels in a conwip controlled job shop. IIE Trans. 32(2), 105–114 (2000)Google Scholar
  19. 19.
    Schlesinger, S., Crosbie, R.E., Gagné, R.E., Innis, G.S., Lalwani, C.S., Loch, J., Sylvester, R.J., Wright, R.D., Kheir, N., Bartos, D.: Terminology for model credibility. Simulation 32(3), 103–104 (1979)CrossRefGoogle Scholar
  20. 20.
    Shen, W., Wang, L., Hao, Q.: Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey. Syst. Man Cybern. Part C: Appl. Rev. IEEE Trans. 36(4), 563–577 (2006)CrossRefGoogle Scholar
  21. 21.
    Spearman, M.L., Woodruff, D.L., Hopp, W.J.: Conwip: a pull alternative to kanban. Int. J. Prod. Res. 28(5), 879–894 (1990)CrossRefGoogle Scholar
  22. 22.
    Spearman, M.L., Zazanis, M.A.: Push and pull production systems: Issues and comparisons. Oper. Res. 40(3), 521–532 (1992)CrossRefzbMATHGoogle Scholar
  23. 23.
    Trentesaux, D.: Distributed control of production systems. Eng. Appl. Artif. Intell. 22(7), 971–978 (2009)Google Scholar
  24. 24.
    Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P.: Reference architecture for holonic manufacturing systems: Prosa. Comput. Ind. 37(3), 255–274 (1998)CrossRefGoogle Scholar
  25. 25.
    Van Dyke Parunak, H.: Agents in overalls: experiences and issues in the development and deployment of industrial agent-based systems. Int. J. Coop. Inf. Syst. 09(03), 209–227 (2000)CrossRefGoogle Scholar
  26. 26.
    Verstraete, P., Saint Germain, B., Valckenaers, P., Van Brussel, H., Belle, J., Hadeli, H.: Engineering manufacturing control systems using prosa and delegate mas. Int. J. Agent-Oriented Softw. Eng. 2(1), 62–89 (2008)CrossRefGoogle Scholar
  27. 27.
    Verstraete, P., Valckenaers, P., Brussel, H.V., Germain, B.S., Hadeli, K., Belle, J.V.: Towards robust and efficient planning execution. Eng. Appl. Artif. Intell. 21(3), 304–314 (2008)CrossRefGoogle Scholar
  28. 28.
    Verstraete, P., Valckenaers, P.: Towards cooperating planning and manufacturing execution systems. IFAC Proc. 39(3), 393–398 (2006). Proceedings of the 12th IFAC Symposium on Information Control Problems in ManufacturingGoogle Scholar
  29. 29.
    Verstraete, P., Valckenaers, P., Saint Germain, B., Van Brussel, H., Hedeli, K.: Integration of planning systems and an agent-oriented mes. Int. J. Manuf. Technol. Manage. 8(1–3), 159–174 (2006)Google Scholar
  30. 30.
    Zambrano Rey, G., Bonte, T., Prabhu, V., Trentesaux, D.: Reducing myopic behavior in FMS control: a semi-heterarchical simulation-optimization approach. Simul. Model. Pract. Theory 46(0), 53–75 (2014). Simulation-Optimization of Complex Systems: Methods and ApplicationsGoogle Scholar
  31. 31.
    Zambrano Rey, G., Pach, C., Aissani, N., Bekrar, A., Berger, T., Trentesaux, D.: The control of myopic behavior in semi-heterarchical production systems: a holonic framework. Eng. Appl. Artif. Intell. 26(2), 800–817 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Mathematics & LogisticsJacobs University Bremen gGmbHBremenGermany

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