USING THE STOCHASTIC APPROACH FRAMEWORK TO MODEL LARGE SCALE MANUFACTURING PROCESSES

Benayadi Nabil, Le Goc Marc, Bouché Philippe

2008

Abstract

Modelling manufacturing process of complex products like electronic ships is crucial to maximize the quality of the production. The Process Mining methods developed since a decade aims at modelling such manufacturing process from the timed messages contained in the database of the supervision system of this process. Such process can complex making difficult to apply the usual Process Mining algorithms. This paper proposes to apply the Stochastic Approach framework to model large scale manufacturing processes. A series of timed messages is considered as a sequence of class occurrences and is represented with a Markov chain from which models are deduced with an abductive reasoning. Because sequences can be very long, a notion of process phase based on a concept of class of equivalence is defined to cut up the sequences so that a model of a phase can be locally produced. The model of the whole manufacturing process is then obtained with the concatenation of the model of the different phases. The paper presents the application of this method to model the electronics chips manufacturing process of the STMicroelectronics Company (France).

References

  1. Agrawal, R., Gunopulos, D., and Leymann, F. (1998). Mining process models from workflow logs. In Sixth International Conference on Extending Database Technology, pages 469-483.
  2. Cook, E. J. and Wolf, A. L. (1998). Discovering models of software processes from event-based data. ACM Transactions on Software Engineering and Methodology, 7:215-249.
  3. Cook, E. J. and Wolf, A. L. (2004). Event-based detection of concurrency. In Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering, volume 53, pages 35-45.
  4. Ghallab, M. (1996). On chronicles: Representation, on-line recognition and learning. Proc. Principles of Knowledge Representation and Reasoning, Aiello, Doyle and Shapiro (Eds.) Morgan-Kauffman, pages 597- 606.
  5. Le Goc, M. (2006). Notion d'observation pour le diagnostic des processus dynamiques: Application à Sachem et à la découverte de connaissances temporelles. Hdr, Faculté des Sciences et Techniques de Saint Jéroˆme.
  6. Le Goc, M., Bouché, P., and Giambiasi, N. (2005). Stochastic modeling of continuous time discrete event sequence for diagnosis. 16th International Workshop on Principles of Diagnosis (DX'05) , California, USA.
  7. Pinter, S. and Golani, M. (2004). Discovering workflow models from activities' lifespans. In Special issue: Process/workflow mining, volume 53, pages 283-296.
  8. Schimm, G. (2004). Mining exact models of concurrent workflows. In Computers in Industry, volume 53(3), pages 265-281.
  9. van der Aalst, W., Weijters, T., and Maruster, L. (2004). Workflow mining: Discovering process models from event logs. In IEEE Transactions on Knowledge and Data Engineering, volume 16, pages 1128-1142.
  10. van der Aalst, W. M. P. and Weijters, A. J. M. M. (2004). Process mining. Special issue of Computers in Industry, 53:231-244.
  11. Weijters, A. and van der Aalst, W. (2003). Rediscovering workflow models from event-based data using little thumb. In Integrated Computer-Aided Engineering, volume 10(2), pages 151-162.
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Paper Citation


in Harvard Style

Nabil B., Goc Marc L. and Philippe B. (2008). USING THE STOCHASTIC APPROACH FRAMEWORK TO MODEL LARGE SCALE MANUFACTURING PROCESSES . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT, ISBN 978-989-8111-53-1, pages 186-191. DOI: 10.5220/0001888801860191


in Bibtex Style

@conference{icsoft08,
author={Benayadi Nabil and Le Goc Marc and Bouché Philippe},
title={USING THE STOCHASTIC APPROACH FRAMEWORK TO MODEL LARGE SCALE MANUFACTURING PROCESSES},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT,},
year={2008},
pages={186-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001888801860191},
isbn={978-989-8111-53-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT,
TI - USING THE STOCHASTIC APPROACH FRAMEWORK TO MODEL LARGE SCALE MANUFACTURING PROCESSES
SN - 978-989-8111-53-1
AU - Nabil B.
AU - Goc Marc L.
AU - Philippe B.
PY - 2008
SP - 186
EP - 191
DO - 10.5220/0001888801860191