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An Intelligent Method for the Scheduling of Cyber Physical Production Systems

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Języki publikacji
EN
Abstrakty
EN
The new industrial era, industry 4.0, leans on Cyber Physical Systems CPS. It is an emergent approach of Production System design that consists of the intimate integration between physical processes and information computation and communication systems. The CPSs redefine the decision-making process in shop floor level to reach an intelligent shop floor control. The scheduling is one of the most important shop floor control functions. In this paper, we propose a cooperative scheduling based on multi-agents modelling for Cyber Physical Production Systems. To validate this approach, we describe a use case in which we implement a scheduling module within a flexible machining cell control tool.
Twórcy
  • Sidi Mohamed Ben Abdellah University, Laboratory of Industrial Technologies, Faculty of Sciences and Technologies, B.P. 2202 – Imouzzer Road, Fez, Morocco
  • Moulay Ismail University, ENSAM-Meknes, Morocco
  • Sidi Mohamed Ben Abdellah University, Superior School of Technology, Morocco
Bibliografia
  • Bouhalouan D., Aissani N., and Beldjilali B. (2009), Proposal of a model for Scheduling an Automated Production System Applications of hybrid genetic algorithms, International Conference on Computer Science and its Applications CIIA.
  • Cardin O. (2016), Contribution to the design, evaluation and implementation of cyber-physical production systems, Habilitation to Direct Research, University of Nantes, IRCCyN Laboratory UMR CNRS 6597, IUT of Nantes.
  • Feldmann S., Rösch S., Schütz D., and Vogel-Heuser B. (2013), Model-Driven Engineering and Semantic Technologies for the Design of Cyber-Physical Systems, chez 11th IFAC Workshop on Intelligent Manufacturing Systems – The International Federation of Automatic Control, São Paulo, Brazil.
  • Festo Didactic (2008), CIROS Supervisions – User Manual.
  • Giebels M. (2000), EtoPlan: a Concept for Concurrent Manufacturing Planning and Control – building holarchies for manufacture-to-order environments, Université de Twente, Netherlands.
  • Hafri Y. and Najid N.M. (2001), Use of the multi-agent approach for real-time control of production systems, 3rd Francophone Conference on Modeling and Simulation, Design, Analysis and Management of Industrial Systems, MOSIM’01, Troyes (France).
  • International Electrotechnical Commission (IEC 62264-3) (2016), Enterprise-control system integration – Part 3: Activity models of manufacturing operations management, ICS : 25.040.40.
  • Ivanov D., Sethi S., Dolgui A., and Sokolov B. (2018), A survey on control theory applications to operational systems, supply chain management, and Industry 4.0, Annual Reviews in Control, Vol. 46, pp. 134-147.
  • Kirn S., Herzog O., Lockemann P. and Spaniol O. (2006), Multi-agent Engineering: theory and applications in enterprises, International Handbooks on Information Systems, Springer Science & Business Media.
  • Klein T. (2008), Active kanban to ensure centralized/ distributed decision-making interoperability Application to a furniture industry, Engineering sciences. Henri Poincaré University – Nancy.
  • Koren Y. and Ulsoy A.G. (1997), Reconfigurable manufacturing systems, Engineering Research Center for Reconfigurable Machining Systems, ERC/RMS report #1. Ann Arbor.
  • Lamrani S., Hasan R. and Martin P. (2003), Design process for Reconfigurable Manufacturing Systems (RMS): Approach using Petri Nets (RP), rev. 5th International Conference Design and Integrated Production. CPI2003, No. 62, ENSAM
  • Meknes. Lee J., Bagheri B. and Kao H.A. (2015), A CyberPhysical Systems architecture for Industry 4.0-based manufacturing systems, Manufacturing Letters 3, pp. 18–23. DOI: 10.1016/j.mfglet.2014.12.001
  • Onori M., Semere D. and Lindberg B. (2009), Evolvable systems: An approach to Self-X Production, Proceedings of the DET/CIRP conference on Digital Enterprise Technology, Hong Kong.
  • Padgham L. and Winikoff M. (2004), Developing Intelligent Agent Systems: A Practical Guide, Chichester: John Wiley & Sons.
  • Rossit D.A., Tohmé F. and Frutos M. (2019), Production planning and scheduling in Cyber-Physical Production Systems: a review, International Journal of Computer Integrated Manufacturing, Vol. 32, No. 14-5, pp. 385–395.
  • Tao F., Zhang M. and Nee A. (2019), Digital twin driven smart manufacturing, 1st ed. Elsevier.
  • Vogel-Heuser B., Lee J. and Leitão P. (2015), Agents enabling cyber-physical production systems, Automatisierungstechnik, De Gruyter Oldenbourg, Vol. 63, No. 110, pp. 777–789.
  • Wooldridge M.J. (2002), An Introduction to Multiagent Systems, New York: John Wiley & Sons.
  • Zhang M., Tao F. and Ayc N. (2021), Digital Twin Enhanced Dynamic Job-Shop Scheduling, Journal of Manufacturing Systems, Vol. 58, pp. 146–156.
  • Zuehlke D. (2010), SmartFactory – Towards a factory-ofthings, Annual Reviews in Control, Vol. 34, pp. 129-138.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-e756c323-d142-4dde-a8ce-cfd230f18a17
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