An agent-based flexible manufacturing system controller with Petri-net enabled algebraic deadlock avoidance
DOI:
https://doi.org/10.31181/rme200101077mKeywords:
Flexible Manufacturing, Petri Net, Resource Allocation System, Deadlock Avoidance, AgentsAbstract
This work focuses on the efficient design of a controller for a Flexible Manufacturing System (FMS) using Agents. The necessary agents were selected and defined according to the Design of Agent-based Production Control Systems (DACS) methodology. The Contract Net Protocol (CNP) was applied for agent communication and interaction. A particular Algebraic Deadlock Avoidance Policy (DAP) is efficiently embedded into CNP. As a result the multi agent system is live and deadlock–free. Feasibility analysis of the controller was performed by exploiting Resource Allocation Systems techniques being defined in the framework of Petri Net theory. The controller is demonstrated in simulation mode in the framework of the Java Agent Development Framework (JADE) system
References
Azizi, A., Poorya-Ghafoorpoor, Y., Al Humairi, A., Alsalmi, M., Al Rashdi, B., Al Zakwani, Z., & ALSheikaili, S. (2018). Applications of control engineering in industry 4.0: utilizing internet of things to design an agent based control architecture for smart material handling system. International Robotics & Automation Journal, 4(4), 253–257. https://doi.org/10.15406/iratj.2018.04.00132
Bellifemine, F., Caire, G., Trucco, T., & Rimassa, G. (2003). JADE programer’s guide, ver. 3.8.
Bellifemine, F. L., Caire, G., & Greenwood, D. (2007). Developing multi-agent systems with JADE. John Wiley.
Bussmann, S., Jennings, N., & Wooldridge, M. J. (2004). Multiagent systems for manufacturing control : a design methodology. Springer.
Cardin, O., Trentesaux, D., Thomas, A., Castagna, P., Berger, T., & Bril El-Haouzi, H. (2017). Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges. Journal of Intelligent Manufacturing, 28(7), 1503–1517. https://doi.org/10.1007/s10845-015-1139-0
Chen, H. F., Wu, N. Q., Li, Z. W., & Qu, T. (2019). On a maximally permissive deadlock prevention policy for automated manufacturing systems by using resource-oriented Petri nets. ISA Transactions, 89, 67–76. https://doi.org/10.1016/j.isatra.2018.11.025
Esmaeilian, B., Behdad, S., & Wang, B. (2016). The evolution and future of manufacturing: A review. Journal of Manufacturing Systems, 39, 79–100. https://doi.org/10.1016/J.JMSY.2016.03.001
Giua, A., & Seatzu, C. (2015). Petri nets for the control of discrete event systems. Software & Systems Modeling, 14(2), 693–701. https://doi.org/10.1007/s10270-014-0425-1
Hsieh, F. S. (2008). Holarchy formation and optimization in holonic manufacturing systems with contract net. Automatica, 44(4), 959–970. https://doi.org/10.1016/j.automatica.2007.09.006
Jimenez, J.-F., Bekrar, A., Zambrano-Rey, G., Trentesaux, D., & Leitão, P. (2017). Pollux: a dynamic hybrid control architecture for flexible job shop systems. International Journal of Production Research, 55(15), 4229–4247. https://doi.org/10.1080/00207543.2016.1218087
Leitão, P., & Karnouskos, S. (2015). Industrial Agents: Emerging Applications of Software Agents in Industry (1st ed.). Elsevier.
Li, Z. W., & Zhou, M. C. (2004). Elementary Siphons of Petri Nets and Their Application to Deadlock Prevention in Flexible Manufacturing Systems. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 34(1), 38–51. https://doi.org/10.1109/TSMCA.2003.820576
Li, Z., & Zhou, M. (2009). Deadlock resolution in automated manufacturing systems : a novel Petri Net approach. Springer.
Lin, R., Yu, Z., Shi, X., Dong, L., & Nasr, E. A. (2020). On Multi-step Look-ahead Deadlock Prediction for Automated Manufacturing Systems Based on Petri Nets. IEEE Access, 1–1. https://doi.org/10.1109/access.2020.3022643
Luo, J., Liu, Z., & Zhou, M. (2019). A petri net based deadlock avoidance policy for flexible manufacturing systems with assembly operations and multiple resource acquisition. IEEE Transactions on Industrial Informatics, 15(6), 3379–3387. https://doi.org/10.1109/TII.2018.2876343
Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., & Ueda, K. (2016). Cyber-physical systems in manufacturing. CIRP Annals, 65(2), 621–641. https://doi.org/https://doi.org/10.1016/j. cirp.2016.06.005
Pérez-Pérez, M., Serrano Bedia, A.-M., López-Fernández, M.-C., & García-Piqueres, G. (2018). Research opportunities on manufacturing flexibility domain: A review and theory-based research agenda. Journal of Manufacturing Systems, 48, 9–20. https://doi.org/10.1016/J.JMSY.2018.05.009
Reveliotis, S. (2017). Logical control of complex resource allocation systems. In Foundations and Trends® in Systems and Control ,vol. 10. Now Publishers.
Rocha, A. D., Barroca, P., Maso, G. D., & Oliveira, J. B. (2017). Environment to simulate distributed agent based manufacturing systems. In Studies in Computational Intelligence (Vol. 694, pp. 405–416). Springer Verlag. https://doi.org/10.1007/978-3-319-51100-9_36
Rosenthal, R. E. (2020). A GAMS Tutorial. https://www.fer.unizg.hr/_download/ repository/gams_ prirucnik%5B1%5D.pdf
Sessego, F., Giua, A., & Seatzu, C. (2008). HYPENS: A matlab tool for timed discrete, continuous and hybrid petri nets. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5062 LNCS, 419–428. https://doi.org/10.1007/978-3-540-68746-7_28
Suzić, N., Forza, C., Trentin, A., & Anišić, Z. (2018). Implementation guidelines for mass customization: current characteristics and suggestions for improvement. Production Planning & Control, 29(10), 856–871. https://doi.org/10.1080/09537287.2018.1485983
Wang, L., & Haghighi, A. (2016). Combined strength of holons, agents and function blocks in cyber-physical systems. Journal of Manufacturing Systems, 40, 25–34. https://doi.org/10.1016/j.jmsy.2016.05.002
Wang, S., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158–168. https://doi.org/10.1016/J.COMNET.2015.12.017