Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Moriwaki, T., Sugimura, N.: Object-oriented modeling of autonomous distributed manufacturing system and its application to real-time scheduling. In: Proc. of the ICOOMS 1992, pp. 207–212 (1992)
Ueda, K.: An approach to bionic manufacturing systems based on DNA-type information. In: Proc. of the ICOOMS 1992, pp. 303–308 (1992)
Hendrik, B., Jo, W., Paul, V., Luc, B., Patrick, P.: Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry 37, 255–274 (1998)
Sugimura, N., Tanimizu, Y., Iwamura, K.: A Study on real-time scheduling for holonic manufacturing system. CIRP journal of manufacturing systems 33(5), 467–475 (2004)
Iwamura, K., Okubo, N., Tanimizu, Y., Sugimura, N.: Real-time scheduling for holonic manufacturing systems based on estimation of future status. International journal of production research 44(18-19), 3657–3675 (2006)
Iwamura, K., Nakano, A., Tanimizu, Y., Sugimura, N.: A study on real-time scheduling for holonic manufacturing systems – simulation for estimation of future status by individual holons. In: Mařík, V., Vyatkin, V., Colombo, A.W. (eds.) HoloMAS 2007. LNCS (LNAI), vol. 4659, pp. 205–214. Springer, Heidelberg (2007)
Fujii, N., Takasu, R., Kobayashi, M., Ueda, K.: Reinforcement learning based product dispatching scheduling in a semiconductor manufacturing system. In: Proc. of the 38th CIRP International seminar on manufacturing systems, CD-ROM (2005)
Sutton, R., Barto, A.: Reinforcement learning: An introduction. MIT Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iwamura, K., Mayumi, N., Tanimizu, Y., Sugimura, N. (2009). A Study on Real-Time Scheduling for Holonic Manufacturing Systems – Determination of Utility Values Based on Multi-agent Reinforcement Learning. In: Mařík, V., Strasser, T., Zoitl, A. (eds) Holonic and Multi-Agent Systems for Manufacturing. HoloMAS 2009. Lecture Notes in Computer Science(), vol 5696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03668-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-03668-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03666-8
Online ISBN: 978-3-642-03668-2
eBook Packages: Computer ScienceComputer Science (R0)