GRAMOSA framework for graphical modelling and simulation-based analysis of complex production processes

ORIGINAL ARTICLE

Abstract

The adequate planning of production processes is a big challenge, in which model-based planning concepts help to manage and to structure the accruing data. By using conceptual process models, like Business Process Modelling Notation, Event-driven Process Chains, etc., the consistent description of material flows is often neglected, meaning that the transformation into material flow simulation models is often impossible. Complex production processes with branched material flows are particularly difficult to model in a simulation driven way. This paper presents the modelling concept GRAMOSA (graphical modelling and simulation-based analysis) as an integrated approach for the material flow-oriented modelling of complex production processes. A new notation as part of an overall factory data model is developed. By using GRAMOSA, the factory data model can be transformed directly into executable simulation models. This concept of automated model generation saves time and reduces transformation failures in comparison to manual programming. The modelling concept and its simulation feasibility is evaluated by a use case, which represents a flexible flow shop scenario with 3 × 3 paralleled machines and inventory control strategy.

Keywords

Production processes engineering Material flow planning Automated model generation Simulation Production planning and control 

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References

  1. 1.
    Scholz-Reiter B, Lütjen M (2007) Production process engineering - modelling and evaluation of process chains for composite manufacturing. Proc. APT’07, Int. Conf. Appl. Prod. Technol. Prod. Aircr. Struct. - Bremen. BIAS Verlag, Bremen, pp 43–51Google Scholar
  2. 2.
    Bergmann S, Strassburger S (2010) Challenges for the automatic generation of simulation models for production systems. Proc. 2010Summer Simul. Multiconference (SummerSim’10). 11-15. July 2010. Ottawa, Canada, pp 545–549Google Scholar
  3. 3.
    Horbach S (2013) Implications of interoperability for factory planning. Adv. Sustain. Compet. Manuf. Syst. Lect. Notes Mech. Eng. 2013Google Scholar
  4. 4.
    Mueller R (2007) Specification and automatic generation of simulation models with aapplications in semiconductor manufacturing. PhD Thesis. Georgia Institute of TechnologyGoogle Scholar
  5. 5.
    Baier J, Krieg R (2008) Automatisierter Modellaufbau für Materialflusssimulationen in der Nutzfahrzeugproduktion. In: Rabe M (ed) Proc. 13. ASIM-Fachtagung “Simulation Produktion und Logistik”, 1./2. Oktober 2008. Fraunhofer IRB Verlag, Berlin, pp 51–60Google Scholar
  6. 6.
    Kumpf A (2001) Anforderungsgerechte modellierung von materialflusssystemen zur planungsbegleitenden simulation. PhD Thesis. Technische Universität MünchenGoogle Scholar
  7. 7.
    Straßburger S, Bergmann S, Müller-Sommer H (2010) Modellgenerierung im Kontext der Digitalen Fabrik - Stand der Technik und Herausforderungen. In: Zülch G, Stock P (eds) Proc. der 14. ASIM-Fachtagung Simul. Produktion und Logistik. Karlsruhe, Karlsruhe, pp 37–44Google Scholar
  8. 8.
    Ehm H, Ponsignon T (2010) Position statement on grand challenges for Discrete Event Logistics Systems (DELS). Proc. 2010 Dagstuhl Semin. Discret. Event Logist. SystGoogle Scholar
  9. 9.
    Mönch L, Lendermann P, McGinnis LF, Schirrmann A (2011) A survey of challenges in modelling and decision-making for discrete event logistics systems. Comput Ind 62:557–567. doi:10.1016/j.compind.2011.05.001 CrossRefGoogle Scholar
  10. 10.
    Wenzel S (2009) Modellbildung und Simulation in Produktion und Logistik - Stand und Perspektiven. In: Elst G (ed) Tagungsband zum ASIM-Treffen STS/GMMS 2009, DASS 2009, 05.- 06.03.2009. Fraunhofer-IRB Verlag, Stuttgart, pp 7–16Google Scholar
  11. 11.
    Huang Y, Seck MD, Verbraeck A (2011) From data to simulation models: Component-based model generation with a data-driven approach. In: S. Jain, R. R. Creasey, J. Himmelspach, K. P. White and MF (ed) Proc. 2011 Winter Simul. Conf. 11-14 Dec. 2011. Phoenix, Arizon, US, pp 3724–3734Google Scholar
  12. 12.
    Ehm H, McGinnis L, Rose O (2009) Are simulation standards in our future? In: Rossetti MD, Hill RR, Johansson B, et al. (eds) Proc. 2009 Winter Simul. Conf. pp 1695–1702Google Scholar
  13. 13.
    Spieckermann S (2005) Diskrete, ereignisorientierte Simulation in Produktion und Logistik – Herausforderungen und Trends. In: Schulze T, Horton G, Preim B, Schlechtweg S (eds) Simul. und Vis. SCS Publishing House, Erlangen, pp 3–14Google Scholar
  14. 14.
    Weimer T, Kapp R, Klemm P, Westkämper E (2008) Integrated Data Management in Factory Planning and Factory Operation. An Information Model and its Implementation. In: Mitsuishi M, Ueda K, Kimura F (eds) Manuf. Syst. Technol. New Front. - 41st CIRP Conf. Manuf. Syst. May 26-28, 2008. Tokyo, Japan, pp 229–234Google Scholar
  15. 15.
    Pitschke J (2010) Unternehmensmodellierung für die Praxis: Eine Einführung in die Darstellung von Unternehmensmodellen. Books on Demand, NorderstedtGoogle Scholar
  16. 16.
    Batarseh O, McGinnis LF (2012) System modelling in SYSML and system analysis in ARENA. In: Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM (eds.) Proceedings of the 2012 Winter Simulation Conference, pp 2924–2935Google Scholar
  17. 17.
    Januszczak J, Hook G (2011) Simulation standard for business process management. In: Jain S, Creasey RR, Himmelspach J, White KP, Fu M (eds.) Proceedings of the 2011 Winter Simulation Conference, pp. 741–751Google Scholar
  18. 18.
    Bergmann S, Stelzer S, Straßburger S (2011) Initialization of simulation models using CMSD. In: Jain S, Creasey RR, Himmelspach J, White KP, Fu M (eds.) Proceedings of the 2011 Winter Simulation Conference, pp. 2228-2239Google Scholar
  19. 19.
    Cetinkaya D, Verbraeck A, Seck MD (2010) Applying a model driven approach to component based modelling and simulation. In: Johansson B, Jain S, Montoya-Torres J, Hugan J, Yucesan E (eds.) Proceedings of the 2010 Winter Simulation Conference, pp. 546–553Google Scholar
  20. 20.
    Guizzardi G, Wagner G (2010) Towards an ontological foundation of discrete event ssimulation. In: Johansson B, Jain S, Montoya-Torres J, Hugan J, Yucesan E (eds.) Proceedings of the 2010 Winter Simulation Conference, pp. 652–664Google Scholar
  21. 21.
    Rooks T (2009) Rechnergestützte Simulationsmodellgenerierung zur dynamischen Absicherung der Montagelogistikplanung bei der Fahrzeugneutypplanung im Rahmen der Digitalen Fabrik. PhD Thesis. Technische Universität Clausthal-ZellerfeldGoogle Scholar
  22. 22.
    Brüggemann H, Müller P (2008) Digitales Wertstromdesign - Digital Value Stream Mapping. In: Rabe M (ed) Adv. Simul. Prod. Logist. Appl. Fraunhofer IRB Verlag, Stuttgart, pp 575–584Google Scholar
  23. 23.
    Zor S, Görlach K, Leymann F (2010) Using BPMN for modelling manufacturing processes. In: W. Sihn PK (ed) Proc. 43rd CIRP Int. Conf. Manuf. Syst. Wien, Graz, pp 515–522Google Scholar
  24. 24.
    Zor S, Leymann F, Schumm D (2011) A Proposal of BPMN Extensions for the Manufacturing Domain. Proc. 44th CIRP Conf. Manuf. Syst. (ICMS 2011). Madison, Wisconsin, pp 1–7Google Scholar
  25. 25.
    Huang E, Ramamurthy R, Mcginnis LF (2007) System and simulation modelling using SysML. In: Henderson SG, Biller B, Hsieh M-H, et al. (eds) Proc. 2007 Winter Simul. Conf. Washington, DC, USA, pp 796–803Google Scholar
  26. 26.
    Huang C (2011) Discrete event system modelling using SysML and Model Transformation. PhD Thesis. Insitute of GeorgiaGoogle Scholar
  27. 27.
    Wu D, Zhang LL, Jiao RJ, Lu RF (2011) SysML-based design chain information modeling for variety management in production reconfiguration. J Intell Manuf 24:575–596. doi:10.1007/s10845-011-0585-6 CrossRefGoogle Scholar
  28. 28.
    Schönherr O, Rose O (2011) A general model description for discrete processes. In: Jain S, Creasey RR, Himmelspach J, et al. (eds) Proc. 2011 Winter Simul. Conf. Baltimore, Maryland, USA, pp 2206–2218Google Scholar
  29. 29.
    Schneider O, Hohenstein F, Günthner WA (2011) Bewertung von Methoden hinsichtlich einer ganzheitlichen Prozessdarstellung. Logist J Not Rev Publ 2011:1–18. doi:10.2195/lj Google Scholar
  30. 30.
    Scholz-Reiter B, Freitag M, de Beer C, Jagalski T (2005) Modelling dynamics of autonomous logistic processes: discrete-event versus continuous approaches. Ann CIRP 55:413–417CrossRefGoogle Scholar
  31. 31.
    Scholz-Reiter B, Freitag M, de Beer C, Jagalski T (2007) Analysing the dynamics caused by autonomously controlled logistic objects. In: ElMaraghy, H.A.; Zaeh MF (ed) Proc. 2nd Int. Conf. Chang. Agil. Reconfigurable Virtual Prod. (CARV 2007). Toronto, Canada, pp 273–280Google Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Bremer Institut für Produktion und Logistik (BIBA)BremenGermany

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