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Autonomous, Adaptive, and Self-Organized Multiagent Systems for the Optimization of Decentralized Industrial Processes

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Intelligent Agents in Data-intensive Computing

Part of the book series: Studies in Big Data ((SBD,volume 14))

Abstract

This chapter presents the concepts, an example implementation, and the evaluation of an autonomous, self-organized, and adaptive multiagent system to optimize industrial processes in dynamic environments. In order to satisfy the rising requirements which result from the Fourth Industrial Revolution and to benefit from the consequent integration of the Internet of Things and Services, the system is designed to link the data of highly decentralized entities to virtual representatives. The goal0 is to mesh complex information and material flows as well as their interdependencies in order to achieve an integrated optimization of production and logistic processes. Due to the high dynamics, the domain of courier and express services provides one of the most challenging environments, in which a high amount of decentralized data and information has to be considered, updated, and processed continuously during operations. The chapter summarizes the state-of-the-art of agent-based approaches in transport logistics and describes the limitations for their application in Industry 4.0 processes. Next, it presents the developed dispAgent approach, the applied coordination and negotiation protocols for the synchronization in highly parallelized negotiations, as well as the solver which have been developed for the individual decision making of the autonomously acting agents. The system is evaluated on two established benchmarks for the Vehicle Routing Problem as well as by a case study with real-world data which was conducted in cooperation with our industrial partner.

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Notes

  1. 1.

    Foundation for Intelligent Physical Agents (for more information see: http://www.fipa.org/ (cited: 22.09.2014)).

  2. 2.

    With the exception of the unlikely case that a negotiation is canceled after the order has been removed from the former vehicle.

  3. 3.

    http://www.openstreetmap.org (cited: 22.09.14).

  4. 4.

    http://www.sintef.no/Projectweb/TOP/VRPTW/ (cited: 22.09.2014).

  5. 5.

    http://www.sintef.no/Projectweb/TOP/VRPTW/Homberger-benchmark/800-customers/(cited: 22.9.2014).

  6. 6.

    http://www.sintef.no/Projectweb/TOP/VRPTW/Homberger-benchmark/400-customers/ (cited: 22.9.2014).

  7. 7.

    http://www.sintef.no/Projectweb/TOP/VRPTW/Solomon-benchmark/100-customers/ (cited: 22.9.2014).

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Acknowledgments

The presented research was partially funded by the German Research Foundation (DFG) under reference number HE 989/14-1 (project Autonomous Courier and Express Services) at the University Bremen, Germany. The simulations were partially performed on the supercomputer at the North German Cooperation for High-Performance Computing (HLRN). This support is gratefully acknowledged by the authors. In addition, we thank our industrial partners for a great cooperation.

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  1. Technologie-Zentrum Informatik Und Informationstechnik, Breme, Germany

    Max Gath, Otthein Herzog & Stefan Edelkamp

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  1. Max Gath
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  2. Otthein Herzog
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Correspondence to Otthein Herzog .

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  1. Department of Computer Science, Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  2. Faculty of Sciences, University of Lisbon, Lisboa, Portugal

    Luís Correia

  3. Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain

    José Manuel Molina

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Gath, M., Herzog, O., Edelkamp, S. (2016). Autonomous, Adaptive, and Self-Organized Multiagent Systems for the Optimization of Decentralized Industrial Processes. In: Kołodziej, J., Correia, L., Manuel Molina, J. (eds) Intelligent Agents in Data-intensive Computing. Studies in Big Data, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-23742-8_4

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