Monostori, László and Csáji, Balázs Csanád and Kádár, Botond Géza (2004) Adaptation and learning in distributed production control. CIRP ANNALS-MANUFACTURING TECHNOLOGY, 53 (1). pp. 349-352. ISSN 0007-8506 10.1016/S0007-8506(07)60714-8

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Abstract

Distributed (agent-based) control architectures offer prospects of reduced complexity, high flexibility and a high robustness against disturbances in manufacturing. However, it has also turned out that distributed control architectures, usually banning all forms of hierarchy, cannot guarantee optimum performance and the system behaviour can be unpredictable. In the paper machine learning approaches such as neurodynamic programming and simulated annealing are described for managing changes and disturbances in manufacturing systems, and to decrease the computational costs of the scheduling process. The results demonstrate the applicability of the proposed solutions, which can contribute to significant improvements in system performance, keeping the known benefits of distributed control.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Research Laboratory on Engineering & Management Intelligence
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	19 Jan 2022 14:15
Last Modified:	19 Jan 2022 14:15
URI:	https://eprints.sztaki.hu/id/eprint/10284

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