Autonomous Decision Policies for Networks of Production Systems

  • Bernd Scholz-Reiter
  • Sergey Dashkowskiy
  • Michael Görges
  • Thomas Jagalski
  • Lars Naujok


Modern production and logistic systems are facing increasing market dynamics: customers demand highly individualized goods, the adherence to due dates becomes critical and stipulated delivery times are decreasing. Particularly logistic networks, e.g. production networks or supply chains, are strongly affected by this trend. On the other hand, production networks have to deal with inherent internal dynamics, which are caused by e.g. machine breakdowns or rush orders. The concept of autonomous control, coming from the theory of self-organization, offers decentralized autonomous decision policies (ADPs), which enable logistic objects to make and execute decision on their own. Due to this kind of decision making, autonomous control aims at a distributed coping with dynamic complexity and, at the same time, at an improvement of the logistic performance. This contribution addresses the concept of autonomous control and the underlying autonomous decision policies as a novel concept for the control of the material flows in networks of coupled production facilities. Moreover, it shows different concepts of modeling and analysis of autonomously controlled networks. To achieve this goal, a dual approach including both, mathematical methods as well as simulation models, is presented. Subsequently, the possibilities to analyze the dynamic behavior of the autonomous logistic system are discussed, i.e., the system’s stability and its logistic performance. Finally, this contribution presents an exemplary case of a production network to demonstrate the practicability of the approach of modeling and analysis of autonomous control for production networks.


Stability Region Discrete Event Simulation Logistic Network Production Network Throughput Time 
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.



This research is funded by the German Research Foundation (DFG) as part of the Collaborative Research Centre 637 ‘Autonomous Cooperating Logistic Processes: A Paradigm Shift and its Limitations’.


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Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Bernd Scholz-Reiter
    • 1
  • Sergey Dashkowskiy
    • 2
  • Michael Görges
    • 1
  • Thomas Jagalski
    • 1
  • Lars Naujok
    • 2
  1. 1.Bremen Institute of Production and Logistics (BIBA)University of BremenBremenGermany
  2. 2.Centre of Industrial MathematicsUniversity of BremenBremenGermany

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