Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling

Released on by Schirin Bär
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  • Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling Book Detail

  • Author : Schirin Bär
  • Release Date : 2022
  • Publisher :
  • Genre :
  • Pages : 0
  • ISBN 13 : 9783658391805
  • File Size : 70,70 MB
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Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling by Schirin Bär PDF Summary

Book Description: The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation. About the author Schirin Bär researched at the RWTH-Aachen University at the Institute for Information Management in Mechanical Engineering (IMA) on the optimization of production control of flexible manufacturing systems using reinforcement learning. As operations manager and previously as an engineer, she developed and evaluated the research results based on real systems.

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Optimization and Learning

Optimization and Learning

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This volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in Februar