Abstract
During the last twenty years, many innovative control architectures of manufacturing systems have been developed and promoted in literature. One of the main attributes, in correlation with the aims of Industry 4.0 paradigm, is to define control architectures where both the actors and the interactions between these actors could cope with an evolution of the environment. To do so, dynamic architectures are being recently developed, where the hierarchy of decision can be jeopardized at any time during the normal behaviour of the system. However, the deployment of such architectures faces major software development issues, that a proper initial modelling could help solving. The objective of this paper is to exhibit good practices in the modelling of dynamic architectures in order to enable an automatic reconfiguration when needed.
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Zezulka, F., Marcon, P., Vesely, I., Sajdl, O.: Industry 4.0 – an Introduction in the phenomenon. IFAC-PapersOnLine 49, 8–12 (2016). https://doi.org/10.1016/j.ifacol.2016.12.002
Barbosa, J., Leitao, P., Trentesaux, D., Colombo, A.W., Karnouskos, S.: Cross benefits from cyber-physical systems and intelligent products for future smart industries. In: 2016 IEEE International Conference on Industrial Informatics (INDIN), pp. 504–509 (2016)
Valckenaers, P.: Perspective on holonic manufacturing systems: PROSA becomes ARTI. Comput. Ind. 120, 103226 (2020). https://doi.org/10.1016/j.compind.2020.103226
Derigent, W., Cardin, O., Trentesaux, D.: Industry 4.0: contributions of holonic manufacturing control architectures and future challenges. J. Intell. Manuf. 32(7), 1797–1818 (2020). https://doi.org/10.1007/s10845-020-01532-x
Bussmann, S., Sieverding, J.: Holonic control of an engine assembly plant: an industrial evaluation. In: 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat. No. 01CH37236), pp. 169–174 (2001)
Le Mortellec, A., Clarhaut, J., Sallez, Y., Berger, T., Trentesaux, D.: Embedded holonic fault diagnosis of complex transportation systems. Eng. Appl. Artif. Intell. 26, 227–240 (2013). https://doi.org/10.1016/j.engappai.2012.09.008
Borangiu, T., Răileanu, S., Oltean, E.V., Silişteanu, A.: Holonic hybrid supervised control of semi-continuous radiopharmaceutical production processes. In: Kondratenko, Y.P., Chikrii, A.A., Gubarev, V.F., Kacprzyk, J. (eds.) Advanced Control Techniques in Complex Engineering Systems: Theory and Applications. SSDC, vol. 203, pp. 229–258. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21927-7_11
Pujo, P., Broissin, N., Ounnar, F.: PROSIS: an isoarchic structure for HMS control. Eng. Appl. Art. Intell. 22, 1034–1045 (2009). https://doi.org/10.1016/j.engappai.2009.01.011
Adam, E., Zambrano, G., Pach, C., Berger, T., Trentesaux, D.: Myopic Behaviour in holonic multiagent systems for distributed control of FMS. In: Corchado, J.M., Pérez, J.B., Hallenborg, K., Golinska, P., Corchuelo, R. (eds.) Trends in Practical Applications of Agents and Multiagent Systems, pp. 91–98. Springer, Berlin, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19931-8_12
Zambrano Rey, G., Bonte, T., Prabhu, V., Trentesaux, D.: Reducing myopic behaviour in FMS control: a semi-heterarchical simulation–optimization approach. Simul. Model. Pract. Theor. 46, 53–75 (2014). https://doi.org/10.1016/j.simpat.2014.01.005
Antzoulatos, N., Castro, E., Scrimieri, D., Ratchev, S.: A multi-agent architecture for plug and produce on an industrial assembly platform. Prod. Eng. Res. Devel. 8(6), 773–781 (2014). https://doi.org/10.1007/s11740-014-0571-x
Cardin, O., Derigent, W., Trentesaux, D.: Evolution of holonic control architectures towards Industry 4.0: a short overview. IFAC-PapersOnLine 51, 1243–1248 (2018). https://doi.org/10.1016/j.ifacol.2018.08.420
Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P.: Reference architecture for holonic manufacturing systems: PROSA. Comput. Ind. 37, 255–274 (1998). https://doi.org/10.1016/S0166-3615(98)00102-X
Cardin, O., Trentesaux, D., Thomas, A., Castagna, P., Berger, T., Bril El-Haouzi, H.: Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges. J. Intell. Manuf. 28(7), 1503–1517 (2015). https://doi.org/10.1007/s10845-015-1139-0
Jimenez, J.F., Bekrar, A., Zambrano-Rey, G., Trentesaux, D., Leitão, P.: Pollux: a dynamic hybrid control architecture for flexible job shop systems. Int. J. Prod. Res. 55, 4229–4247 (2017). https://doi.org/10.1080/00207543.2016.1218087
Pach, C., Berger, T., Bonte, T., Trentesaux, D.: ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling. Comput. Ind. 65, 706–720 (2014). https://doi.org/10.1016/j.compind.2014.02.005
Borangiu, T., Răileanu, S., Berger, T., Trentesaux, D.: Switching mode control strategy in manufacturing execution systems. Int. J. Prod. Res. (2015). https://doi.org/10.1080/00207543.2014.935825
Barbosa, J., Leitão, P., Adam, E., Trentesaux, D.: Dynamic self-organization in holonic multi-agent manufacturing systems: the ADACOR evolution. Comput. Ind. 66, 99–111 (2015). https://doi.org/10.1016/j.compind.2014.10.011
André, P., Cardin, O.: Aggregation patterns in holonic manufacturing systems. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds.) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA 2021, pp. 3–15. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99108-1_1
André, P., Ardourel, G., Messabihi, M.: Component service promotion: contracts, mechanisms and safety. In: Barbosa, L.S., Lumpe, M. (eds.) FACS 2010. LNCS, vol. 6921, pp. 145–162. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27269-1_9
André, P., Azzi, F., Cardin, O.: Heterogeneous communication middleware for digital twin based cyber manufacturing systems. In: Borangiu, T., Trentesaux, D., Leitão, P., Giret Boggino, A., Botti, V. (eds.) SOHOMA 2019. SCI, vol. 853, pp. 146–157. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27477-1_11
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Authors thank financial support from the French National Research Agency (ANR) under the McBIM project, grant number ANR-17-CE10-0014.
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Derigent, W., David, M., André, P., Cardin, O. (2023). Generic Aggregation Model for Reconfigurable Holonic Control Architecture – The GARCIA Framework. In: Borangiu, T., Trentesaux, D., Leitão, P. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2022. Studies in Computational Intelligence, vol 1083. Springer, Cham. https://doi.org/10.1007/978-3-031-24291-5_32
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DOI: https://doi.org/10.1007/978-3-031-24291-5_32
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