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

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.

Autorentext

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.

Inhalt
Introduction.- Requirements for Production Scheduling in Flexible Manufacturing.- Reinforcement Learning as an Approach for Flexible Scheduling.- Concept for Multi-Resources Flexible Job-Shop Scheduling.- Multi-Agent Approach for Reactive Scheduling in Flexible Manufacturing.- Empirical Evaluation of the Requirements.- Integration into a Flexible Manufacturing System.- Bibliography.