Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 21

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

The Syntax of a Multi-Level Production Process Modeling Language

, , , , ,

DOI: http://dx.doi.org/10.15439/2020F176

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 751760 ()

Full text

Abstract. The fourth industrial revolution introduces changes in traditional manufacturing systems and creates basis for a lot-size-one production. The complexity of production processes is significantly increased, alongside the need to enable efficient process simulation, execution, monitoring, real-time decision making and control. The main goal of our research is to define a methodological approach and a software solution in which the Model-Driven Software Development (MDSD) principles and Domain-Specific Modeling Languages (DSMLs) are used to create a framework for the formal description and automatic execution of production processes. In that way production process models are used as central artefacts to manage the production. In this paper, we propose a DSML which can be used to create production process models that are suitable for automatic generation of executable code. The generated code is used for automatic execution of production processes within a simulation or a shop floor.

References

  1. H. Lasi, P. Fettke, H.-G. Kemper, T. Feld, and M. Hoffmann, “Industry 4.0,” Bus. Inf. Syst. Eng., vol. 6, no. 4, pp. 239–242, Aug. 2014, http://dx.doi.org/https://doi.org/10.1007/s12599-014-0334-4.
  2. K. Dorofeev, S. Profanter, J. Cabral, P. Ferreira, and A. Zoitl, “Agile Operational Behavior for the Control-Level Devices in Plug&Produce Production Environments,” in Proceedings of 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain, 2019, pp. 49–56, http://dx.doi.org/https://doi.org/10.1109/ETFA.2019.8869208.
  3. D. Gorecky, M. Schmitt, M. Loskyll, and D. Zuhlke, “Human-machine-interaction in the industry 4.0 era,” in Proceedings of 2014 12th IEEE International Conference on Industrial Informatics (INDIN), Porto Alegre RS, Brazil, Jul. 2014, pp. 289–294, http://dx.doi.org/https://doi.org/10.1109/INDIN.2014.6945523.
  4. S. M. Fallah, S. Wolny, and M. Wimmer, “Towards model-integrated service-oriented manufacturing execution system,” in 2016 1st International Workshop on Cyber-Physical Production Systems (CPPS), Vienna, Austria, Apr. 2016, pp. 1–5, http://dx.doi.org/https://doi.org/10.1109/CPPS.2016.7483917.
  5. M. Vještica, V. Dimitrieski, M. Pisarić, S. Kordić, S. Ristić, and I. Luković, “Towards a formal description and automatic execution of production processes,” in Proceedings of 2019 IEEE 15th International Scientific Conference on Informatics, Poprad, Slovakia, Nov. 2019, pp. 463–468, http://dx.doi.org/https://doi.org/10.1109/Informatics47936.2019.9119314.
  6. M. Vještica, V. Dimitrieski, M. Pisarić, S. Kordić, S. Ristić, and I. Luković, “Towards a Formal Specification of Production Processes Suitable for Automatic Execution,” Open Comput. Sci., p. 20, May 2020, to be published.
  7. M. Pisarić, V. Dimitrieski, M. Vještica, and G. Krajoski, “Towards a Non-Disruptive System for Dynamic Orchestration of the Shop Floor,” in IFIP Advances in Information and Communication Technology (AICT), Novi Sad, Serbia, 2020, vol. 592, pp. 1–8, http://dx.doi.org/https://doi.org/10.1007/978-3-030-57997-5_54.
  8. V. Dimitrieski, “Model-Driven Technical Space Integration Based on a Mapping Approach,” Ph.D. Thesis, University of Novi Sad, Faculty of Technical Sciences, Serbia, 2017.
  9. M. Mernik, J. Heering, and A. M. Sloane, “When and how to develop domain-specific languages,” ACM Comput. Surv., vol. 37, no. 4, pp. 316–344, Dec. 2005, http://dx.doi.org/https://doi.org/10.1145/1118890.1118892.
  10. Q. Qi and F. Tao, “Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison,” IEEE Access, vol. 6, pp. 3585–3593, 2018, http://dx.doi.org/https://doi.org/10.1109/ACCESS.2018.2793265.
  11. C. Leyh, S. Martin, and T. Schäffer, “Industry 4.0 and Lean Production – A Matching Relationship? An analysis of selected Industry 4.0 models,” in Proceedings of 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Sep. 2017, vol. 11, pp. 989–993, http://dx.doi.org/https://doi.org/10.15439/2017F365.
  12. T. Qu, S. P. Lei, Z. Z. Wang, D. X. Nie, X. Chen, and G. Q. Huang, “IoT-based real-time production logistics synchronization system under smart cloud manufacturing,” Int. J. Adv. Manuf. Technol., vol. 84, no. 1–4, pp. 147–164, Apr. 2016, http://dx.doi.org/https://doi.org/10.1007/s00170-015-7220-1.
  13. S. Vaidya, P. Ambad, and S. Bhosle, “Industry 4.0 – A Glimpse,” in Procedia Manufacturing, Maharashtra, India, 2018, vol. 20, pp. 233–238, http://dx.doi.org/https://doi.org/10.1016/j.promfg.2018.02.034.
  14. J. Wan, H. Cai, and K. Zhou, “Industrie 4.0: Enabling Technologies,” in Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, Harbin, 2015, pp. 135–140, http://dx.doi.org/https://doi.org/10.1109/ICAIOT.2015.7111555.
  15. L. D. Xu, E. L. Xu, and L. Li, “Industry 4.0: state of the art and future trends,” Int. J. Prod. Res., vol. 56, no. 8, pp. 2941–2962, Apr. 2018, http://dx.doi.org/https://doi.org/10.1080/00207543.2018.1444806.
  16. L. D. Xu, “Enterprise Systems: State-of-the-Art and Future Trends,” IEEE Trans. Ind. Inform., vol. 7, no. 4, pp. 630–640, Nov. 2011, http://dx.doi.org/https://doi.org/10.1109/TII.2011.2167156.
  17. H. Ahn and T.-W. Chang, “Measuring Similarity for Manufacturing Process Models,” in IFIP Advances in Information and Communication Technology (AICT), Cham, Aug. 2018, vol. 536, pp. 223–231, http://dx.doi.org/https://doi.org/10.1007/978-3-319-99707-0_28.
  18. J. Jiao, M. M. Tseng, Q. Ma, and Y. Zou, “Generic Bill-of-Materials-and-Operations for High-Variety Production Management,” Concurr. Eng., vol. 8, no. 4, pp. 297–321, Dec. 2000, http://dx.doi.org/https://doi.org/10.1177/1063293X0000800404.
  19. Korean Standards Service Network (KSSN), “KS A 3002 Standard.” https://www.kssn.net/en/ (accessed Apr. 05, 2020).
  20. M. Witsch and B. Vogel-Heuser, “Towards a Formal Specification Framework for Manufacturing Execution Systems,” IEEE Trans. Ind. Inform., vol. 8, no. 2, pp. 311–320, May 2012, http://dx.doi.org/https://doi.org/10.1109/TII.2012.2186585.
  21. A. Wortmann, O. Barais, B. Combemale, and M. Wimmer, “Modeling Languages in Industry 4.0: An Extended Systematic Mapping Study,” Softw. Syst. Model., vol. 19, pp. 67–94, Jan. 2020, http://dx.doi.org/https://doi.org/10.1007/s10270-019-00757-6.
  22. S. Zor, D. Schumm, and F. Leymann, “A Proposal of BPMN Extensions for the Manufacturing Domain,” in Proceedings of the 44th CIRP International Conference on Manufacturing Systems, Madison, Wisconsin, USA, 2011, pp. 1–7.
  23. M. Lütjen and D. Rippel, “GRAMOSA framework for graphical modelling and simulation-based analysis of complex production processes,” Int. J. Adv. Manuf. Technol., vol. 81, no. 1–4, pp. 171–181, May 2015, http://dx.doi.org/https://doi.org/10.1007/s00170-015-7037-y.
  24. S. Meyer, A. Ruppen, and L. Hilty, “The Things of the Internet of Things in BPMN,” in Advanced Information Systems Engineering Workshops. CAiSE 2015. Lecture Notes in Business Information Processing, Stockholm, Sweden, 2015, vol. 215, pp. 285–297, http://dx.doi.org/https://doi.org/10.1007/978-3-319-19243-7_27.
  25. R. Petrasch and R. Hentschke, “Towards an Internet-of-Things-aware Process Modeling Method - An Example for a House Suveillance System Process Model,” in Proceedings of 2nd Management and Innovation Technology International Conference (MITiCON2015), Bangkok, Thailand, 2015, pp. 168–172.
  26. R. Petrasch and R. Hentschke, “Process modeling for industry 4.0 applications: Towards an industry 4.0 process modeling language and method,” in Proceedings of 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), Khon Kaen, Thailand, Jul. 2016, pp. 1–5, http://dx.doi.org/https://doi.org/10.1109/JCSSE.2016.7748885.
  27. S. Schönig, L. Ackermann, S. Jablonski, and A. Ermer, “IoT meets BPM: a bidirectional communication architecture for IoT-aware process execution,” Softw. Syst. Model., Mar. 2020, http://dx.doi.org/https://doi.org/10.1007/s10270-020-00785-7.
  28. B. Weissenberger, S. Flad, X. Chen, S. Rosch, T. Voigt, and B. Vogel-Heuser, “Model driven engineering of manufacturing execution systems using a formal specification,” in Proceedings of 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), Luxembourg, Sep. 2015, pp. 1–8, http://dx.doi.org/https://doi.org/10.1109/ETFA.2015.7301430.
  29. D. Steinberg, F. Budinsky, M. Paternostro, and E. Merks, EMF: Eclipse Modeling Framework, 2nd ed. Upper Saddle River, NJ, USA: Addison-Wesley Professional, 2008.
  30. “Eclipse Sirius Documentation.” https://www.eclipse.org/sirius/doc/ (accessed Mar. 19, 2020).
  31. I. Dejanovic, M. Tumbas, G. Milosavljevic, and B. Perisic, “Comparison of Textual and Visual Notations of DOMMLite Domain-Specific Language,” in Local Proceedings of the Fourteenth East-European Conference on Advances in Databases and Information Systems, Novi Sad, Serbia, Sep. 2010, pp. 131–136.
  32. Object Management Group, “Business Process Model and Notation, Version 2.0.2,” Technical Report, 2014.
  33. M. Kocbek, G. Jost, M. Hericko, and G. Polancic, “Business process model and notation: The current state of affairs,” Comput. Sci. Inf. Syst., vol. 12, no. 2, pp. 509–539, 2015, http://dx.doi.org/https://doi.org/10.2298/CSIS140610006K.