NDLI: A centralized reinforcement learning approach for proactive scheduling in manufacturing

Content Provider	IEEE Xplore Digital Library
Author	Shuhui Qu Tianshu Chu Jie Wang Leckie, J. Weiwen Jian
Copyright Year	2015
Description	Author affiliation: Center for Sustainable Dev. & Global Competitiveness, Stanford Univ., Stanford, CA, USA (Shuhui Qu; Tianshu Chu; Jie Wang; Leckie, J.; Weiwen Jian)
Abstract	Due to rapid development of information and communications technology (ICT) and the impetus for more effective, efficient and adaptive manufacturing, the concept of ICT based advanced manufacturing has increasingly become a prominent research topic across academia and industry during recent years. One critical aspect of advanced manufacturing is how to incorporate real time information and then optimally schedule manufacturing processes with multiple objectives. Due to its complexity and the need for adaptation, the manufacturing scheduling problem presents challenges for utilizing advanced ICT and thus calls for new approaches. The paper proposes a centralized reinforcement learning approach for optimally scheduling of a manufacturing system of multi-stage processes and multiple machines for multiple types of products. The approach, which employs learning and control algorithms to enable real time cooperation of each processing unit inside the system, is able to adaptively respond to dynamic scheduling changes. More specifically, we first formally define the scheduling problem through the construction of an objective function and related heuristic constraints for the underlying manufacturing tasks. Next, to effectively deal with the problem we defined, we maintain a distributed weighted vector to capture the cooperative pattern of massive action space and apply the reinforcement-learning approach to achieve the optimal policies for a set of processing machines according to a real time production environment, including dynamic requests for various products. Numerical experiments demonstrate that compared to different heuristic methods and multi-agent algorithms, the proposed centralized reinforcement learning method can provide more reliable solutions for the scheduling problem.
Starting Page	1
Ending Page	8
File Size	7140480
Page Count	8
File Format	PDF
e-ISBN	9781467379298
DOI	10.1109/ETFA.2015.7301417
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-08
Publisher Place	Luxembourg
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Job shop scheduling Learning (artificial intelligence) Processor scheduling Optimal scheduling Manufacturing Real-time systems centralized system scheduling reinforcement learning multi-stage multi-product advanced manufacturing real-time information
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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