TY - GEN
T1 - A study on real-time scheduling for holonic manufacturing systems - Determination of utility values based on multi-agent reinforcement learning
AU - Iwamura, Koji
AU - Mayumi, Norihisa
AU - Tanimizu, Yoshitaka
AU - Sugimura, Nobuhiro
PY - 2009
Y1 - 2009
N2 - This paper deals with a real-time scheduling method for holonic manufacturing systems (HMS). In the previous paper, a real-time scheduling method based on utility values has been proposed and applied to the HMS. In the proposed method, all the job holons and the resource holons firstly evaluate the utility values for the cases where the holon selects the individual candidate holons for the next machining operations. The coordination holon secondly determine a suitable combination of the resource holons and the job holons which carry out the next machining operations, based on the utility values. Multi-agent reinforcement learning is newly proposed and implemented to the job holons and the resource holons, in order to improve their capabilities for evaluating the utility values of the candidate holons. The individual job holons and resource holons evaluate the suitable utility values according to the status of the HMS, by applying the proposed learning method.
AB - This paper deals with a real-time scheduling method for holonic manufacturing systems (HMS). In the previous paper, a real-time scheduling method based on utility values has been proposed and applied to the HMS. In the proposed method, all the job holons and the resource holons firstly evaluate the utility values for the cases where the holon selects the individual candidate holons for the next machining operations. The coordination holon secondly determine a suitable combination of the resource holons and the job holons which carry out the next machining operations, based on the utility values. Multi-agent reinforcement learning is newly proposed and implemented to the job holons and the resource holons, in order to improve their capabilities for evaluating the utility values of the candidate holons. The individual job holons and resource holons evaluate the suitable utility values according to the status of the HMS, by applying the proposed learning method.
KW - Coordination
KW - Holonic Manufacturing Systems
KW - Multi-agent Reinforcement Learning
KW - Real-time Scheduling
UR - http://www.scopus.com/inward/record.url?scp=70349307055&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-03668-2_13
DO - 10.1007/978-3-642-03668-2_13
M3 - Conference contribution
AN - SCOPUS:70349307055
SN - 364203666X
SN - 9783642036668
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 135
EP - 144
BT - Holonic and Multi-Agent Systems for Manufacturing - 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2009, Proceedings
T2 - 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2009
Y2 - 31 August 2009 through 2 September 2009
ER -