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Autonomous control of production networks using a pheromone approach

Author

Listed:
  • Armbruster, D.
  • de Beer, C.
  • Freitag, M.
  • Jagalski, T.
  • Ringhofer, C.

Abstract

The flow of parts through a production network is usually pre-planned by a central control system. Such central control fails in presence of highly fluctuating demand and/or unforeseen disturbances. To manage such dynamic networks according to low work-in-progress and short throughput times, an autonomous control approach is proposed. Autonomous control means a decentralized routing of the autonomous parts themselves. The parts’ decisions base on backward propagated information about the throughput times of finished parts for different routes. So, routes with shorter throughput times attract parts to use this route again. This process can be compared to ants leaving pheromones on their way to communicate with following ants.

Suggested Citation

  • Armbruster, D. & de Beer, C. & Freitag, M. & Jagalski, T. & Ringhofer, C., 2006. "Autonomous control of production networks using a pheromone approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(1), pages 104-114.
    • Handle: RePEc:eee:phsmap:v:363:y:2006:i:1:p:104-114
    DOI: 10.1016/j.physa.2006.01.052
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    References listed on IDEAS

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    2. Audrey Dussutour & Vincent Fourcassié & Dirk Helbing & Jean-Louis Deneubourg, 2004. "Optimal traffic organization in ants under crowded conditions," Nature, Nature, vol. 428(6978), pages 70-73, March.
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    Cited by:

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    2. Göttlich, S. & Herty, M. & Ringhofer, C., 2010. "Optimization of order policies in supply networks," European Journal of Operational Research, Elsevier, vol. 202(2), pages 456-465, April.
    3. Eduardo Alarcon-Gerbier & Zarina Chokparova & Nassim Ghondaghsaz & Wanqi Zhao & Hani Shahmoradi-Moghadam & Uwe Aßmann & Orçun Oruç, 2022. "Software-Defined Mobile Supply Chains: Rebalancing Resilience and Efficiency in Production Systems," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    4. Jin, Wen-Long, 2015. "Point queue models: A unified approach," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 1-16.
    5. Nassim Ghondaghsaz & Zarina Chokparova & Sven Engesser & Leon Urbas, 2022. "Managing the Tension between Trust and Confidentiality in Mobile Supply Chains," Sustainability, MDPI, vol. 14(4), pages 1-25, February.
    6. Michael Görges & Michael Freitag, 2022. "Design and Evaluation of an Integrated Autonomous Control Method for Automobile Terminals," Logistics, MDPI, vol. 6(4), pages 1-27, October.
    7. Becker, Till & Illigen, Christoph & McKelvey, Bill & Hülsmann, Michael & Windt, Katja, 2016. "Using an agent-based neural-network computational model to improve product routing in a logistics facility," International Journal of Production Economics, Elsevier, vol. 174(C), pages 156-167.
    8. Görges, Michael & Freitag, Michael, 2019. "Modeling autonomously controlled automobile terminal processes," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 186-214, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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