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Distributed learning and control for manufacturing systems scheduling
Joonki Hong
,
Vittal Prabhu
Harold and Inge Marcus Department of Industrial and Manufacturing Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
1
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Scopus citations
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Dive into the research topics of 'Distributed learning and control for manufacturing systems scheduling'. Together they form a unique fingerprint.
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Keyphrases
Controller
100%
Manufacturing Systems
100%
Control Problem
100%
System Scheduling
100%
Distributed Learning Algorithm
100%
Distributed Control Algorithm
100%
Just-in-time Production
100%
Learning Approaches
50%
Performance Improvement
50%
Machine Control
50%
Setup Cost
50%
Control Approach
50%
Learning Components
50%
Time of Arrival
50%
Distributed Arrival Time Control
50%
Multi-objective Scheduling
50%
Due Date
50%
Time Machine
50%
Q-learning
50%
Scheduling Problem
50%
Shop Floor Management
50%
Engineering
Control Algorithm
100%
Shop Floor
100%
Gas Fuel Manufacture
100%
Learning Algorithm
100%
Production Time
100%
Arrival Time
100%
Performance Improvement
50%
Setup Cost
50%
One Step
50%
Q-Learning
50%
Markov Decision Process
50%
Reinforcement Learning
50%
Computer Science
Distributed Learning
100%
Learning Algorithm
50%
Control Algorithm
50%
Just-in-Time
50%
Performance Improvement
25%
Learning Approach
25%
Multiobjective
25%
Scheduling Problem
25%
Control Approach
25%
Reinforcement Learning
25%
Markov Decision Process
25%
Learning Component
25%