Abstract
Nowadays, industrials are seeking for models and methods that are not only able to provide efficient overall production performance, but also for reactive systems facing a growing set of unpredicted events. One important research activity in that field focuses on holonic/multi-agent control systems that couple predictive/proactive and reactive mechanisms into agents/holons. Meanwhile, not enough attention is paid to the optimization of this coupling. The aim of this paper is to depict the main research challenges that are to be addressed before expecting a large industrial dissemination. Relying on an extensive review of the state of the art, three main challenges are highlighted: the estimation of the future performances of the system in reactive mode, the design of efficient switching strategies between predictive and reactive modes and the design of efficient synchronization mechanisms to switch back to predictive mode.
Similar content being viewed by others
References
Barbosa, J., Leitão, P., Adam, E., & Trentesaux, D. (2012). Nervousness in dynamic self-organized holonic multi-agent systems. Highlights on Practical Applications of Agents and Multi-Agent Systems, Advances in Intelligent and Soft Computing, 156, 9–17.
Barbosa, J., Leitão, P., Adam, E., & Trentesaux, D. (2015). Dynamic self-organization in holonic multi-agent manufacturing systems: The ADACOR evolution. Computers in Industry, 66, 99–111. doi:10.1016/j.compind.2014.10.011.
Basile, F., Chiacchio, P., & De Tommasi, G. (2009). An efficient approach for online diagnosis of discrete event systems. IEEE Transactions on Automatic Control, 54(4), 748–759. doi:10.1109/TAC.2009.2014932.
Berry, W. L., Whybark, D. C., & Vollmann, T. E. (1991). Manufacturing planning and control systems (Business One Irwin/APICS Series in Production Management) (3rd ed.). Burr Ridge: Richard D Irwin.
Böhnlein, D., Schweiger, K., & Tuma, A. (2011). Multi-agent-based transport planning in the newspaper industry. International Journal of Production Economics, 131(1), 146–157. doi:10.1016/j.ijpe.2010.04.006.
Borangiu, T., Răileanu, S., Berger, T., & Trentesaux, D. (2015). Switching mode control strategy in manufacturing execution systems. International Journal of Production Research, 53(7), 1950–1963. doi:10.1080/00207543.2014.935825.
Bussmann, S., & Schild, K. (2001). An agent-based approach to the control of flexible production systems. In Eighth IEEE International Conference on Emerging Technologies and Factory Automation (Vol. 2, pp. 481–488).
Cabasino, M. P., Giua, A., & Seatzu, C. (2010). Fault detection for discrete event systems using Petri nets with unobservable transitions. Automatica, 46(9), 1531–1539. doi:10.1016/j.automatica.2010.06.013.
Cardin, O., & Castagna, P. (2009). Using online simulation in holonic manufacturing systems. Engineering Applications of Artificial Intelligence, 22(7), 1025–1033. Accessed 8 April 2014.
Cardin, O., & Castagna, P. (2011). Proactive production activity control by online simulation. International Journal of Simulation and Process Modelling, 6(3), 177. doi:10.1504/IJSPM.2011.044766.
Cardin, O., Mebarki, N., & Pinot, G. (2013). A study of the robustness of the group scheduling method using an emulation of a complex FMS. International Journal of Production Economics, 146(1), 199–207. doi:10.1016/j.ijpe.2013.06.023.
Chaari, T., Chaabane, S., Loukil, T., & Trentesaux, D. (2011). A genetic algorithm for robust hybrid flow shop scheduling. International Journal of Computer Integrated Manufacturing, 24(9), 821–833. doi:10.1080/0951192X.2011.575181.
Chan, F. T. S., Jiang, B., & Tang, N. K. H. (2000). The development of intelligent decision support tools to aid the design of flexible manufacturing systems. International Journal of Production Economics, 65(1), 73–84. doi:10.1016/S0925-5273(99)00091-2.
Dilts, D. M., Boyd, N. P., & Whorms, H. H. (1991). The evolution of control architectures for automated manufacturing systems. Journal of Manufacturing Systems, 10(1), 79–93. doi:10.1016/0278-6125(91)90049-8.
Dotoli, M., Pia Fanti, M., Mangini, A. M., & Ukovich, W. (2011). Identification of the unobservable behaviour of industrial automation systems by Petri nets. Control Engineering Practice, 1(9), 958–966. doi:10.1016/j.conengprac.2010.09.004.
El Haouzi, H., Pétin, J.-F., & Thomas, A. (2009). Design and validation of a product-driven control system based on a six sigma methodology and discrete event simulation. Production Planning & Control, 20(6), 510–524. doi:10.1080/09537280902938589.
Ferrarini, L., Veber, C., Luder, A., Peschke, J., Kalogeras, A., Gialelis, J., et al. (2006). Control Architecture for Reconfigurable Manufacturing Systems: the PABADIS’PROMISE approach. In IEEE Conference on Emerging Technologies and Factory Automation, 2006. ETFA ’06 (pp. 545–552). Presented at the IEEE Conference on Emerging Technologies and Factory Automation, 2006. ETFA ’06. doi:10.1109/ETFA.2006.355427.
Ghezail, F., Pierreval, H., & Hajri-Gabouj, S. (2010). Analysis of robustness in proactive scheduling: A graphical approach. Computers & Industrial Engineering, 58(2), 193–198. doi:10.1016/j.cie.2009.03.004.
Herrera, C., Thomas, A., Belmokhtar, S., & Pannequin, R. (2011). A viable system model for product-driven systems. In: IESM 2011. Metz, France. https://hal.archives-ouvertes.fr/hal-00607682. Accessed 19 Jan 2015.
Herrera, C., Thomas, A., & Parada, V. (2014). A product-driven system approach for multilevel decisions in manufacturing planning and control. Production & Manufacturing Research, 2(1), 756–766. doi:10.1080/21693277.2014.949895.
Idghamishi, A. M., & Hashtrudi Zad, S. (2004). Fault diagnosis in hierarchical discrete-event systems. In 43rd IEEE Conference on Decision and Control, 2004. CDC (Vol. 1, pp. 63–68). Presented at the 43rd IEEE Conference on Decision and Control, 2004. CDC. doi:10.1109/CDC.2004.1428607.
Kiritsis, D., Kadiri, S. E., Perdikakis, A., Milicic, A., Alexandrou, D., & Pardalis, K. (2013). Design of fundamental ontology for manufacturing product lifecycle applications. In C. Emmanouilidis, M. Taisch, & D. Kiritsis (Eds.), Advances in production management systems. Competitive manufacturing for innovative products and services (pp. 376–382). Springer: Berlin. http://link.springer.com/chapter/10.1007/978-3-642-40352-1_47. Accessed 4 June 2015.
Kuehnle, H. (2007). Post mass production paradigm (PMPP) trajectories. Journal of Manufacturing Technology Management, 18(8), 1022–1037. doi:10.1108/17410380710828316.
Leitao, P., Barbosa, J., Vrba, P., Skobelev, P., Tsarev, A., & Kazanskaia, D. (2013). Multi-agent system approach for the strategic planning in ramp-up production of small lots. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 4743–4748). Presented at the 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Manchester, UK. doi:10.1109/SMC.2013.807.
Leitao, P., Colombo, A. W., & Restivo, F. J. (2005). ADACOR: A collaborative production automation and control architecture. Intelligent Systems, IEEE, 20(1), 58–66.
Leitão, P., & Restivo, F. (2006). ADACOR: A holonic architecture for agile and adaptive manufacturing control. Computers in Industry, 57(2), 121–130. doi:10.1016/j.compind.2005.05.005.
Li, M., Bril-El Haouzi, H., Thomas, A., & Guidat, A. (2015). Fuzzy decision-making method for product holons encountered emergency breakdown in product-driven system: An industrial case. In T. Borangiu, A. Thomas, & D. Trentesaux (Eds.), Service orientation in holonic and multi-agent manufacturing. Berlin: Springer.
Lüder, A., Peschke, J., Sauter, T., Deter, S., & Diep, D. (2004). Distributed intelligence for plant automation based on multi-agent systems: The PABADIS approach. Production Planning & Control, 15(2), 201–212. doi:10.1080/09537280410001667484.
Matthias, F., Jäger, T., Turrin, C., Petrali, P., Pagani, A., & Leitao, P. (2013). Implementation of a methodology for consideration of product quality within discrete manufacturing. In B. Natalia (Ed.), Manufacturing modelling, management, and control (Vol. 7, pp. 863–868). Presented at the Manufacturing Modelling, Management, and Control, St. Petersburg, Russia. doi:10.3182/20130619-3-RU-3018.00181.
Miche, M., Baumann, K., Golenzer, J., & Brogle, M. (2012). A simulation model for evaluating distributed storage services for smart product systems. In A. Puiatti & T. Gu (Eds.), Mobile and ubiquitous systems: Computing, Networking, and Services (pp. 162–173). Springer: Berlin. http://link.springer.com/chapter/10.1007/978-3-642-30973-1_14. Accessed 10 April 2015.
Muhl, E., Charpentier, P., & Chaxel, F. (2003). Optimization of physical flows in an automotive manufacturing plant: Some experiments and issues. Engineering Applications of Artificial Intelligence, 16(4), 293–305. doi:10.1016/S0952-1976(03)00081-2.
Novas, J. M., Belle, J. V., Germain, B. S., & Valckenaers, P. (2013). A collaborative framework between a scheduling system and a holonic manufacturing execution system. In T. Borangiu, A. Thomas, & D. Trentesaux (Eds.), Service orientation in holonic and multi agent manufacturing and robotics (pp. 3–17). Springer: Berlin. http://link.springer.com/chapter/10.1007/978-3-642-35852-4_1. Accessed 2 April 2015.
Pach, C., Berger, T., Bonte, T., & Trentesaux, D. (2014). ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling. Computers in Industry. doi:10.1016/j.compind.2014.02.005.
Paoli, A., & Lafortune, S. (2008). Diagnosability analysis of a class of hierarchical state machines. Discrete Event Dynamic Systems, 18(3), 385–413. doi:10.1007/s10626-008-0044-5.
Prabhu, V. V., & Duffie, N. A. (1996). Modelling and analysis of heterarchical manufacturing systems using discontinuous differential equations. CIRP Annals-Manufacturing Technology, 45(1), 445–448. doi:10.1016/S0007-8506(07)63099-6.
Pujo, P., Broissin, N., & Ounnar, F. (2009). PROSIS: An isoarchic structure for HMS control. Engineering Applications of Artificial Intelligence, 22(7), 1034–1045. doi:10.1016/j.engappai.2009.01.011.
Raileanu, S., Parlea, M., Borangiu, T., & Stocklosa, O. (2012). A JADE environment for product driven automation of holonic manufacturing. In T. Borangiu, A. Thomas, & D. Trentesaux (Eds.), Service Orientation in Holonic and Multi-Agent Manufacturing Control (pp. 265–277). Springer: Berlin. http://link.springer.com/chapter/10.1007/978-3-642-27449-7_20. Accessed 2 April 2015.
Reinhart, G., & Englehardt, P. (2013). Modular configuration of an RFID-based hybrid control architecture for a situational shop floor control. Industrial and Systems Engineering Review, 1(1), 31–39.
Rolón, M., & Martínez, E. (2012). Agent-based modeling and simulation of an autonomic manufacturing execution system. Computers in Industry, 63(1), 53–78. doi:10.1016/j.compind.2011.10.005.
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555–1575. doi:10.1109/9.412626.
Shahzad, A., & Mebarki, N. (2012). Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem. Engineering Applications of Artificial Intelligence, 25(6), 1173–1181. doi:10.1016/j.engappai.2012.04.001.
Stellingwerff, L., & Pazienza, G. E. (2014). An agent-based architecture to model and manipulate context knowledge. In Y. Demazeau, F. Zambonelli, J. M. Corchado, & J. Bajo (Eds.), Advances in practical applications of heterogeneous multi-agent systems. The PAAMS collection (pp. 256–267). Springer. http://link.springer.com/chapter/10.1007/978-3-319-07551-8_22. Accessed 5 April 2015.
Thomas, A., El Haouzi, H., Klein, T., Belmokhtar, S., & Herrera, C. (2009). Architecture de systèmes contrôlés par le produit pour un environnement de juste à temps. Journal Européen des Systèmes Automatisés, 43(4–5), 513–535. doi:10.3166/jesa.43.513-535.
Thomas, A., Trentesaux, D., & Valckenaers, P. (2012). Intelligent distributed production control. Journal of Intelligent Manufacturing, 23(6), 2507–2512. doi:10.1007/s10845-011-0601-x.
Thomas, P., & Thomas, A. (2011). Multilayer perceptron for simulation models reduction: Application to a sawmill workshop. Engineering Applications of Artificial Intelligence, 24(4), 646–657. doi:10.1016/j.engappai.2011.01.004.
Valckenaers, P., Van Brussel, H., Verstraete, P., Saint Germain, B., & Karuna, H. (2007). Schedule execution in autonomic manufacturing execution systems. Journal of Manufacturing Systems, 26(2), 75–84. doi:10.1016/j.jmsy.2007.12.003.
Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., & Peeters, P. (1998). Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry, 37(3), 255–274. doi:10.1016/S0166-3615(98)00102-X.
Verstraete, P., Saint Germain, B., Valckenaers, P., Van Brussel, H., Belle, J., & Hadeli, H. (2008). Engineering manufacturing control systems using PROSA and delegate MAS. International Journal of Agent-Oriented Software Engineering, 2(1), 62–89. doi:10.1504/IJAOSE.2008.0168.
Yang, T., Ma, J., Hou, Z.-G., Peng, G., & Tan, M. (2008). A multi-agent architecture based cooperation and intelligent decision making method for multirobot systems. In M. Ishikawa, K. Doya, H. Miyamoto, & T. Yamakawa (Eds.), Neural Information Processing (pp. 376–385). Springer: Berlin. http://link.springer.com/chapter/10.1007/978-3-540-69162-4_39. Accessed 2 April 2015.
Zambrano, G., Pach, C., Aissani, N., Berger, T., Trentesaux, D. (2011). An approach for temporal myopia reduction in Heterarchical Control Architectures. In 2011 IEEE International Symposium on Industrial Electronics (ISIE) (pp. 1767–1772). Presented at the. (2011). IEEE International Symposium on Industrial. Electronics (ISIE). doi:10.1109/ISIE.2011.5984424.
Zaytoon, J., & Lafortune, S. (2013). Overview of fault diagnosis methods for discrete event systems. Annual Reviews in Control, 37(2), 308–320. doi:10.1016/j.arcontrol.2013.09.009.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Cardin, O., Trentesaux, D., Thomas, A. et al. Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges. J Intell Manuf 28, 1503–1517 (2017). https://doi.org/10.1007/s10845-015-1139-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-015-1139-0