Title:
FACTORY SIMULATION-BASED SCHEDULING SYSTEM USING REINFORCEMENT LEARNING
Document Type and Number:
WIPO Patent Application WO/2022/085939
Kind Code:
A1
Abstract:
The present invention relates to a factory simulator-based scheduling system using reinforcement learning, wherein processes in a factory environment, in which products are produced when a workflow including a large number of sequentially related processes is configured and the processes in the workflow are carried out, are scheduled by training a neural network agent that determines the next task action when given the current state of the workflow. The system comprises: a neural network agent having at least one neural network which, when the state of a factory workflow (hereinafter, workflow state) is received, outputs the next task to be carried out in the state, wherein the neural network is trained by a reinforcement learning method; a factory simulator for simulating the factory workflow; and a reinforcement learning module which simulates the factory workflow with the factory simulator, extracts reinforcement learning data from simulation results, and trains the neural network of the neural network agent with the extracted reinforcement learning data.
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Inventors:
YUN YOUNG MIN (KR)
LEE HO YEOUL (KR)
LEE HO YEOUL (KR)
Application Number:
PCT/KR2021/012157
Publication Date:
April 28, 2022
Filing Date:
September 07, 2021
Export Citation:
Assignee:
NEUROCORE CO LTD (KR)
International Classes:
G05B19/418; G06N3/08
Foreign References:
| KR20200094577A | 2020-08-07 | |||
| KR20180110940A | 2018-10-11 | |||
| KR20200092447A | 2020-08-04 | |||
| US20190325304A1 | 2019-10-24 | |||
| KR101345068B1 | 2013-12-26 |
Attorney, Agent or Firm:
HONESTY & PATENT IP LAW FIRM (KR)
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