A methodology for cloud manufacturing architecture in the context of industry 4.0

• Autorzy: Talhi E. , Huet J.-C. , Fortineau V. , Lamouri S.

Identyfikatory

DOI

10.24425/bpasts.2020.131849

• Języki publikacji: EN

Abstrakty

EN

This paper deals with a methodology for the implementation of cloud manufacturing (CM) architecture. CM is a current paradigm in which dynamically scalable and virtualized resources are provided to users as services over the Internet. CM is based on the concept of coud computing, which is essential in the Industry 4.0 trend. A CM architecture is employed to map users and providers of manufacturing resources. It reduces costs and development time during a product lifecycle. Some providers use different descriptions of their services, so we propose taking advantage of semantic web technologies such as ontologies to tackle this issue. Indeed, robust tools are proposed for mapping providers’ descriptions and user requests to find the most appropriate service. The ontology defines the stages of the product lifecycle as services. It also takes into account the features of coud computing (storage, computing capacity, etc.). The CM ontology will contribute to intelligent and automated service discovery. The proposed methodology is inspired by the ASDI framework (analysis–specification–design–implementation), which has already been used in the supply chain, healthcare and manufacturing domains. The aim of the new methodology is to propose an easy method of designing a library of components for a CM architecture. An example of the application of this methodology with a simulation model, based on the CloudSim software, is presented. The result can be used to help the industrial decision-makers who want to design CM architectures.

Słowa kluczowe

EN

cloud manufacturing; cloud-based services; service-oriented architecture; Industry 4.0; ontology

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2020
• Tom: Vol. 68, nr 2
• Strony: 271--284
• Opis fizyczny: Bibliogr. 46 poz., rys.

Twórcy

autor

Talhi E.

• EIGSI, La Rochelle, France

autor

Huet J.-C.

• EFREI Paris, Villejuif, France

autor

Fortineau V.

• LAMIH, CNRS, Arts et métiers ParisTech, Paris, France

autor

Lamouri S.

• LAMIH, CNRS, Arts et métiers ParisTech, Paris, France

Bibliografia

• [1] Y. Lu, “Industry 4.0: A survey on technologies, applications and open research issues,” J. Ind. Inf. Integr., 6, 1–10 (2017). https://doi.org/10.1016/j.jii.2017.04.005
• [2] X. Xu, “From Coud Computing to Coud Manufacturing,” Rob. Comput. Integr. Manuf., 28(1), 75–86 (2012).
• [3] S. Terzi, “Elements of Product Lifecycle Management: Definitions, Open Issues and Reference Models,” doctoral thesis, Université Henri Poincaré-Nancy I, May 2005. https://tel.archives-ouvertes.fr/tel-00009559
• [4] O. Cardin, “Classification of cyber-physical production systems applications: Proposition of an analysis framework,” Comput. Ind., 104, 11–21 (2019). https://doi.org/10.1016/j.compind.2018.10.002
• [5] Z. Zhang, X. Wang, X. Zhu, Q. Cao, and F. Tao, “Cloud manufacturing paradigm with ubiquitous robotic system for product customization,” Rob. Comput. Integr. Manuf., 60, 12–22 (2019). https://doi.org/10.1016/j.rcim.2019.05.015
• [6] M. Zaborowski, “Data processing in self-controlling enterprise processes,” Bull. Pol. Ac.: Tech., 67(1), 3–20 (2019).
• [7] D. Mazur, A. Paszkiewicz, M. Bolanowski, B.G., and M. Oleksy, “Analysis of possible SDN use in the rapid proto- typing process as part of the industry 4.0,” Bull. Pol. Ac.: Tech., 67 (1), 21–30 (2019).
• [8] L. D. Xu, E. L. Xu, and L. Li, “Industry 4.0: state of the art and future trends,” Int. J. Prod. Res., 56 (8), 2941–2962 (2018). https://doi.org/10.1080/00207543.2018.1444806
• [9] H. Van Brussel, J. Wyns, P. Valckenaers, L. Bongaerts, and P. Peeters, “Reference architecture for holonic manufacturing systems: Prosa,” Comput. Ind., 37 (3), 255–274 (1998).
• [10] A. Koestler et al., The ghost in the machine. Hutchinson London, 1967.
• [11] J.-C. Huet, J.-L. Paris, K. Kouiss, and M. Gourgand, “A new reengineering methodology for the product-driven system applied to the medication-use process,” Decis. Support Syst., 55(2), 599–615 (2013), 1. Analytics and Modeling for Better Health Care 2. Decision Making in Healthcare.
• [12] F. Tao, L. Zhang, V. Venkatesh, Y. Luo, and Y. Cheng, “Cloud manufacturing: a computing and service-oriented manufacturing model,” J. Engineering Manufacture, 225(10), 1969–1976 (2011).
• [13] C. Danjou, L. Rivest, and R. Pellerin, “Douze positionnements stratégiques pour l’industrie 4.0: entre processus, produit et service, de la surveillance à l’autonomie (twelve strategic positioning for industry 4.0: between process, product and service, from monitoring to autonomy.),” in CIGI, 2017.
• [14] M. Rüßmann, M. Lorenz, P. Gerbert, M. Waldner, J. Justus, P. Engel, and M. Harnisch, “Industry 4.0: The future of productivity and growth in manufacturing industries,” Boston Consulting Group, 9(1), 54–89 (2015).
• [15] G. Zellner, “A structured evaluation of business process improvement approaches,” Bus. Process Manag. J., 17(2), 203–237 (2011).
• [16] M. Gourgand and P. Kellert, “An object-oriented methodology for manufacturing system modelling,” in Summer Computer Simulation Conference, Reno, Nevada, USA, July 1992, pp. 1123–1128.
• [17] M. Chabrol, D. Sarramia, and N. Tchernev, “Urban traffic systems modelling methodology,” Int. J. Prod. Econ., 99, 156–176 (2006).
• [18] H.E. Haouzi, A. Thomas, and J. Pétin, “Contribution to reusability and modularity of manufacturing systems simulation models: Application to distributed control simulation within {DFT} context,” Int. J. Prod. Econ., 112(1), 48–61 (2008).
• [19] J.-C. Huet and I. El Abbassi, “Green Coud Computing modelling methodology,” in Utility and Cloud Computing (UCC), 2013 IEEE/ACM 6th International Conference on, Dec 2013, pp. 339–344.
• [20] A. Ahlam, “Construction of scheduling methods and tools for the phosphate mining industry in the context of Lean Management,” doctoral thesis, Université Paris 10, December 2018. https://www.theses.fr/s148905
• [21] E.J. Ghomi, A.M. Rahmani, and N.N. Qader, “Cloud manufacturing: challenges, recent advances, open research issues, and future trends,” Int. J. Adv. Manuf. Technol., 102 (9), 3613–3639, (2019). https://doi.org/10.1007/s00170‒019‒03398‒7
• [22] A. Talhi, J.-C. Huet, V. Fortineau, and S. Lamouri, “Toward an ontology-based architecture for Coud Manufacturing,” in Service Orientation in Holonic and Multi-agent Manufacturing, ser. Studies in Computational Intelligence, vol. 594, pp. 187–195 T. Borangiu, Thomas, and D. Trentesaux, Eds. Springer International Publishing, 2015.
• [23] A. Talhi, J. Huet, V. Fortineau, and S. Lamouri, “Towards a Coud Manufacturing systems modeling methodology,” in 15th IFAC Symposium on Information Control Problems in Manufacturing, 48(3), 288–293 (2015). https://doi.org/10.1016/j.ifacol.2015.06.096
• [24] X.V. Wang and X.W. Xu, “An interoperable solution for Coud Manufacturing,” Rob. Comput. Integr. Manuf., 29 (4), 232–247 (2013).
• [25] Y. Lu, X. Xu, and J. Xu, “Development of a hybrid manufacturing cloud,” J. Manuf. Syst., 33(4), 551–566 (2014).
• [26] T.Y. Lin, C. Yang, C. Zhuang, Y. Xiao, F. Tao, G. Shi, and C. Geng, “Multi-centric management and optimized allocation of manufacturing resource and capability in Coud Manufacturing system,” J. Engineering Manufacture, 231(12), 2159–2172 (2016). https://doi.org/10.1177/0954405415624364
• [27] H. Bouzary and F.F. Chen, “Service optimal selection and composition in Coud Manufacturing: a comprehensive survey,” Int. J. Adv. Manuf. Technol., 97, 795–808 (2018).
• [28] Y. Lu and X. Xu, “A semantic web-based framework for service composition in a Coud Manufacturing environment,” J. Manuf. Syst., 42, 69–81 (2017). https://doi.org/10.1016/j.jmsy.2016.11.004
• [29] J. Son, A.V. Dastjerdi, R.N. Calheiros, X. Ji, Y. Yoon, and R. Buyya, “Cloudsimsdn: Modeling and simulation of software-defined cloud data centers,” in Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2015), Shenzhen, China, May 2015.
• [30] D. Kliazovich, P. Bouvry, and S.U. Khan, “GreenCloud: a packet-level simulator of energy-aware Coud Computing data centers,” J. Supercomput., 62 (3), 1263–1283 (2012).
• [31] S.-H. Lim, B. Sharma, G. Nam, E.K. Kim, and C.R. Das, “MDC-Sim: a multi-tier data center simulation, platform,” in Cluster Computing and Workshops, 2009. CLUSTER’09. IEEE International Conference on. IEEE, 2009, pp. 1–9.
• [32] R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A. De Rose, and R. Buyya, “CloudSim: a toolkit for modeling and simulation of Coud Computing environments and evaluation of resource provisioning algorithms,” Software. Pract. Exper., 41(1), 23–50 (2011).
• [33] E. Barbierato, M. Gribaudo, M. Iacono, and A. Jakóbik, “Exploiting cloudsim in a multiformalism modeling approach for cloud based systems,” Modeling and Simulation of Cloud Computing and Big Data. Simul. Model. Pract. Theory, 93, 133–147 (2019), https://doi.org/10.1016/j.simpat.2018.09.018
• [34] K. Ma, A. Bagula, C. Nyirenda, and O. Ajayi, “An iot-based fog computing model,” Sensors, 19 (12), 2783 (2019). https://doi.org/10.3390/s19122783
• [35] H.A. Simon, “The architecture of complexity,” Proceedings of the American Philosophical Society, 106(6), 467–482, (1962). http://www.jstor.org/stable/985254
• [36] F. Moscato, R. Aversa, B. Di Martino, T. Fortis, and V. Munteanu, “An analysis of mosaic ontology for cloud resources annotation,” in Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on, Sept 2011, pp. 973–980.
• [37] W. Tsai, X. Sun, Q. Huang, and H. Karatza, “An ontology-based collaborative service-oriented simulation framework with microsoft robotics studio®,” Simul. Model. Pract. Theory, 16(9), 1392–1414 (2008).
• [38] V. Fortineau, T. Paviot, and S. Lamouri, “Improving the interoperability of industrial information systems with description logic-based models—the state of the art,” Comput. Ind., 64(4), 363–375 (2013).
• [39] S. Bussmann, “An agent-oriented architecture for holonic manufacturing control,” in Proceedings of First International Workshop on IMS, 1998, 1998. http://www.stefan-bussmann.de/files/ims98.ps
• [40] S.V. Fortineau, X. Fiorentini, T. Paviot, L. Louis-Sidney, and Lamouri, “Expressing formal rules within ontology-based models using swrl: an application to the nuclear industry,” Int. J. Prod. Lifecycle. Manag., 7(1), 75–93 (2014).
• [41] A. Talhi, V. Fortineau, J.-C. Huet, and S. Lamouri, “Ontology for Coud Manufacturing based product lifecycle management,” J. Intell. Manuf., 30(5), 2171– 2192 (2019). https://doi.org/10.1007/s10845‒017‒1376‒5
• [42] C. Atkinson and K. Kiko, “A detailed comparison of uml and owl,” Report from University of Mannheim, 2008. https://ub-madoc.bib.uni-mannheim.de/1898/
• [43] K. Främling, T. Ala-Risku, M. Kärkkäinen, and J. Holmström, “Design patterns for managing product life cycle information,” Communications of the ACM, 50(6), 75–79 (2007).
• [44] Y. Lu, T. Peng, and X. Xu, “Energy-efficient cyber- physical production network: Architecture and technologies,” Comput. Ind. Eng., 129, 56–66 (2019). https://doi.org/10.1016/j.cie.2019.01.025
• [45] K. Bilal, O. Khalid, A. Erbad, and S.U. Khan, “Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers,” Comput. Networks, 130, 94–120 (2018). https://doi.org/10.1016/j.comnet.2017.10.002
• [46] V. Jain and B. Kolla, “Case study: Could costing discoveries for the end to end solution,” in COLLABORATE 19, Technology and Applications Forum for the Oracle Community, 2019.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).