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Intelligent Agents for Social and Learning Logistics Systems

In: Dynamics in Logistics

Author

Listed:
  • Otthein Herzog

    (Tongji University
    University of Bremen)

  • Ingo J. Timm

    (German Research Center for Artificial Intelligence (DFKI), SDS Branch Trier, Cognitive Social Simulation
    Trier University, FB4 – Business Informatics I
    LogDynamics, Center for Computing Technologies of the University of Bremen)

Abstract

The digitalization of logistics processes is often based on distributed models and decentralized control. As these logistics models constitute an important part of Industrie 4.0 concepts they must be powerful enough to cover dynamic processes and must enable a host of functions such as goal-oriented, reactive, pro-active, communicative, cooperative, competitive, and learning behaviors. In addition, these distributed models must allow for simulating, planning, allocating, scheduling, and optimizing logistics tasks. This implies that they must be able to act through communication channels with each other thus establishing logistics social communities. Multiagent Systems (MAS) have been around for more than 30 years and lend themselves to the implementation of these distributed models needed for autonomous and cooperating logistics processes. It will be described and also demonstrated by three case studies why MAS are well suited for social and learning logistics systems. It will be shown how the resulting distributed MAS models provide the required functionalities for production and transportation logistics including the handling of dynamic local events as an essential feature for the successful planning, scheduling, optimizing, monitoring, and control of global logistics processes.

Suggested Citation

  • Otthein Herzog & Ingo J. Timm, 2021. "Intelligent Agents for Social and Learning Logistics Systems," Springer Books, in: Michael Freitag & Herbert Kotzab & Nicole Megow (ed.), Dynamics in Logistics, pages 91-107, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-88662-2_5
    DOI: 10.1007/978-3-030-88662-2_5
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