Planning in Dynamic, Distributed and Non-automatized Production Systems

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 376)


We present a framework for realization of a decentralized decision support system for deployment in production scenarios with a low level of automation, such as in multi-site construction. Furthermore we present the insights concerning the improvements of the production process when applying online simulation based optimization methods in that scenarios. Our solution shows how to realize a central control and decision support station with special focus on easy connection to the possible multiple construction sites and on high usability for nonexperts. Our framework is able to connect to a large number of modeling, simulation and analysis tools. For sake of usability it turned out, that only dialogue-based communication with the end user seems applicable in those scenarios, where often only simple devices are present.


Multi-site construction Decision support Predictive simulation 


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  1. 1.
    Pohle, K., Gottwald, F.-J.: Baustellenmanagement: Einsparpotenziale, Trends und Strategien. In: Technical report, MANAGEMENT ENGINEERS, VDMA Arbeitsgemeinschaft Grossanlagenbau (2009)Google Scholar
  2. 2.
    Taghezout, N., Bessedik, I., Adla, A.: Application to resource allocation problems in a flow-shop manufacturing system. Journal of Decision Systems 20(4), 443–466 (2011)Google Scholar
  3. 3.
    Goodwin, P., Önkal, D.D., Thomson, M.: Do forecasts expressed as prediction intervals improve production planning? European Journal of Operational Research 205(1), 195–201 (2010)CrossRefGoogle Scholar
  4. 4.
    Macal, C.M., North, M.J.: Tutorial on agent-based modelling and simulation. J. Simulation 4(3), 151–162 (2010)CrossRefGoogle Scholar
  5. 5.
    Renna, P.: Multi-agent based scheduling in manufacturing cells in a dynamic environment. International Journal of Production Research 49(5), 1285–1301 (2011)CrossRefGoogle Scholar
  6. 6.
    Webster, M., Muhlemann, A.P., Alder, C.: Decision support for the scheduling of subcontract manufacture. International Journal of Operations & Production Management 20(10), 1218–1237 (2000)CrossRefGoogle Scholar
  7. 7.
    Becker, M., Szczerbicka, H.: Integration of multi-class queueing networks in generalised stochastic petri nets. In: 2001 IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 1137–1142 (2001)Google Scholar
  8. 8.
    Aissani, N., Bekrar, A., Trentesaux, D., Beldjilali, B.: Dynamic scheduling for multi-site companies: A decisional approach based on reinforcement multi-agent learning. Journal of Intelligent Manufacturing 23(2), 2513–2529 (2012)CrossRefGoogle Scholar
  9. 9.
    Timpe, C.H., Kallrath, J.: Optimal planning in large multi-site production networks. European Journal of Operational Research 126(2), 422–435 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Chung, S., Lau, H., Choy, K., Ho, G., Tse, Y.: Application of genetic approach for advanced planning in multi-factory environment. International Journal of Production Economics 127(2), 300–308 (2010). Supply Chain Planning and Configuration in the Global ArenaCrossRefGoogle Scholar
  11. 11.
    Gnoni, M., Iavagnilio, R., Mossa, G., Mummolo, G., Di Leva, A.: Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry. International Journal of Production Economics 85(2), 251–262 (2003)CrossRefGoogle Scholar
  12. 12.
    Becker, M., Balci, S., Szczerbicka, H.: Predictive simulation based support system for resource failure management in multi-site production environments. In: 2014 International Conference on Control, Decision and Information Technologies (CoDIT), pp. 526–530. IEEE (2014)Google Scholar
  13. 13.
    Lütjen, M., Karimi, H.-R.: Approach of port inventory control system for the offshore installation of wind turbines. In: Chung, J.S., Langen, I., Hong, S.Y., Prinsenberg, S.J. (eds.) The Proceedings of The Twenty-second (2012) International Offshore and Polar Engineering Conference (ISOPE). Renewable Energy (Offshore Wind and Ocean), International Society of Offshore and Polar Engineers (ISOPE), Cupertino, California, USA, pp. 502–508 (2012)Google Scholar
  14. 14.
    Barlow, E., Öztürk, D.T., Revie, M., Boulougouris, E., Day, A.H., Akartunal, K.: Exploring the impact of innovative developments to the installation process for an offshore wind farm. Ocean Engineering 109, 623–634 (2015)CrossRefGoogle Scholar
  15. 15.
    Kaiser, M.J., Snyder, B.F.: Modeling offshore wind installation vessel day-rates in the United States. Maritime Economics and Logistics 14(2), 220–248 (2012)CrossRefGoogle Scholar
  16. 16.
    Becker, M., Balci, S., Szczerbicka, H.: A framework for decision support in systems with a low level of automation. In: International Conference on Computers and Industrial Engineering, CIE45Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Modeling and Simulation GroupUniversity HannoverHannoverGermany
  2. 2.BIBA, Bremer Institute of Production and LogisticsUniversity of BremenBremenGermany

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