Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
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Product Details
Brand
Springer Nature
Manufacturer
N/A
Part Number
0
GTIN
9783658391782
Condition
New
Product 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.
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