Pheromone-based coordination for manufacturing system control
- 269 Downloads
A pheromone-based coordination approach, which comes from the collective behavior of ant colonies for food foraging, is applied to control manufacturing system in this paper, aiming at handling dynamic changes and disturbances. The pheromone quantum of manufacturing cell is calculated inversely proportional to the cost, which can guarantee a minimal cost to process the orders. This approach has the capacity for optimization model to automatically find efficient routing paths for processing orders and to reduce communication overhead which exists in contract net protocol in shop floor control system. A prototype system is developed, and experiments confirm that pheromone-based coordination approach has excellent control performance and adaptability to disturbances in shop floor.
KeywordsCoordination and control Pheromone Dynamic adaptation Shop floor control
Unable to display preview. Download preview PDF.
- Baker, A. D. (1991). Manufacturing control with a market-driven contract net. Ph.D. Thesis. Troy, NY.: Electrical Computer and Systems Engineering Department, Rensselaer Polytechic Institute.Google Scholar
- Ben-Arieh, D., & Chopra, M. (1997). A game theoretic approach to real-time distributed shop floor control. In Sixth industrial engineering research conference Proceedings, Miami Beach, USA.Google Scholar
- Botelho, S. C., & Alami, R. (1999). A scheme for multi-robot cooperation through negotiated task allocation and achievement. IEEE international conference on robotics and automation, Detroit, USA.Google Scholar
- Camazine S., Deneubourg F., Franks N. R., Sneyd J., Theraulaz G., Bonabeau E. (2001) Self-organization in biological systems. Princeton University Press, PrincetonGoogle Scholar
- Cicirello, V. A., & Smith, S. F. (2001a). Insect societies and manufacturing. Working notes of the IJCAI-01 workshop on artificial intelligence and manufacturing. Seattle.Google Scholar
- Cicirello, V. A., & Smith, S. F. (2001b). Ant colony control for autonomous decentralized shop floor routing. In Proceedings of fifth international symposium on autonomous decentralized systems, Dallas, USA.Google Scholar
- Colorni, A., Dorigo, M., & Maniesso, V. (1991). Distributed optimization by ant colonies. Toward a practice of autonomous systems. In Proceedings of the first European conference on artificial life, Paris, France.Google Scholar
- Guan, Z. L., Lei, M., Wu, B., Wu, Y., & Yang, S. Z. (1995). Application of decentralized cooperative problem solving in dynamic flexible scheduling. In Proceedings of SPIE, SPIE1995, Wuhan, China.Google Scholar
- Ouelhadj, D., Hanachi, C., Bouzouia, B., Moualek, A., & Farhi, A. (1999). Multi-contract net protocol for dynamic scheduling in flexible manufacturing systems (FMS). In Proceedings of the 1999 IEEE international conference on robotics and automation, ICRA99, Detroit, USA.Google Scholar
- Vojdani, N. (1996). Distributed manufacturing control using fuzzy contract net. IEEE international conference on fuzzy systems, New Orleans, USA.Google Scholar
- Walsh, W. E., Wellman, M. P., Wurman, P. R., & MacKie-Mason, J. K. (1998). Some economics of market-based distributed scheduling. Eighteenth international conference on distributed computing systems, Amsterdam.Google Scholar