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Outline

Title
Abstract
Introduction
Input Data Management in Des
Core Manufacturing Simulation Data
Introduction of the Ke Tool
Design Description / Core Objects
Data Extraction
Data Processing
Test Implementation of the Ke Tool in a Case Study
Description of Case Study
Discussion
Conclusions
References
All Topics
Computer Science
Modeling and Simulation

An Open Source Tool for Automated Input Data in Simulation

Profile image of Cathal HeaveyCathal Heavey

2015, International Journal of Simulation Modelling

https://doi.org/10.2507/IJSIMM14(4)3.306
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13 pages

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Abstract

Discrete Event Simulation (DES) is one of the most effective tools for planning, designing and improving material flows in production. One of the main weaknesses of operating DES is the exertion needed and costs spent on collecting and handling the input data from different organisation's data resources. To tackle the problem of the time consuming input data process for DES projects an Open Source (OS) tool, called Knowledge Extraction (KE) tool was developed. The tool reads data from several organisations' resources; analyses it and outputs it in a format that is applicable to be used by a simulation tool, all conducted in one automated process. The primary, readable to simulation software, output format follows the Core Manufacturing Simulation Data (CMSD). This paper presents the KE tool and a test implementation, as a first step towards the validation of the tool in a real case study in the medical industry.

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References (27)

  1. Banks, J.; Carson, J. S.; Nelson, B. L. (1996). Discrete-Event System Simulation, Prentice-Hall, Upper Saddle River
  2. Skoogh, A.; Johansson, B. (2007). Time-consumption analysis of input data activities in discrete event simulation projects, Proceedings of the 2007 Swedish Production Symposium
  3. Perera, T.; Liyanage, K. (2000). Methodology for rapid identification and collection of input data in the simulation of manufacturing systems, Simulation Practice and Theory, Vol. 7, No.7, 645- 656, doi:10.1016/S0928-4869(99)00020-8
  4. Onggo, B. S. S.; Hill, J.; Brooks, R. J. (2013). A survey on data identification and collection in simulation projects, Proceedings of the 27 th European Simulation and Modelling Conference, 23- 25
  5. Skoogh, A.; Johansson, B. (2009). Mapping of time-consumption during input data management activities, Simulation News Europe (SNE), Vol. 19, No. 2, 39-46
  6. Leong, S.; Lee, Y. T.; Riddick, F. (2006). A Core Manufacturing Simulation Data Information Model for Manufacturing Applications, Simulation Interoperability Workshop, Simulation Interoperability and Standards Organization, 1-7
  7. SISO (2010). Simulation Interoperability Standards Organization (SISO) Standard for : Core Manufacturing Simulation Data ─ UML Model. Core Manufacturing Simulation Data Product Development Group, Simulation Interoperability Standards Organization
  8. Skoogh, A.; Perera, T.; Johansson, B. (2012). Input data management in simulation -Industrial practices and future trends, Simulation Modelling Practice and Theory, Vol. 29, 181-192, doi:10.1016/j.simpat.2012.07.009
  9. Skoogh, A.; Johansson, B. (2008). A methodology for input data management in discrete event simulation projects, Proceedings of the 2008 Winter Simulation Conference, 1727-1735
  10. Law, A. M.; Kelton, W. D. (2000). Simulation Modelling and Analysis (3 rd ed.), McGraw-Hill, Boston
  11. McNally, P.; Heavey, C. (2004). Developing simulation as a desktop resource, International Journal of Computer Integrated Manufacturing, Vol. 17, No. 5, 435-450, doi:10.1080/ 09511920310001654283
  12. Hollocks, B. W. (2001). Discrete-event simulation: an inquiry into user practice, Simulation Practice and Theory, Vol. 8, No. 6-7, 451-471, doi:10.1016/S0928-4869(01)00028-3
  13. Robinson, S. (2004). Simulation: The Practice of Model Development and Use, John Wiley & Sons Ltd, Chichester
  14. Lehtonen, J.-M.; Seppala, U. (1997). A methodology for data gathering and analysis in a logistics simulation project, Integrated manufacturing systems, Vol. 8, No. 6, 351-358, doi:10.1108/ 09576069710188760
  15. Eloranta, E.; Räisänen, J. (1986). Reconsidering ten conventions of production management, Proceedings of the Conference on New Technologies for Production Management Systems, Tokyo, 23-38
  16. Lee, Y.-T. T.; Riddick, F. H.; Johansson, B. J. I. (2011). Core Manufacturing Simulation Data -a manufacturing simulation integration standard: overview and case studies, International Journal of Computer Integrated Manufacturing, Vol. 24, No. 8, 689-709, doi:10.1080/0951192X. 2011.574154
  17. Bengtsson, N.; Shao, G.; Johansson, B.; Lee, Y. T.; Leong, S.; Skoogh, A.; Mclean, C. (2009). Input data management methodology for discrete event simulation, Proceedings of the 2009 Winter Simulation Conference, 1335-1344
  18. Skoogh, A.; Johansson, B.; Stahre, J. (2012). Automated input data management: evaluation of a concept for reduced time consumption in discrete event simulation, Simulation, Vol. 88, No. 11, 1279-1293, doi:10.1177/0037549712443404
  19. ISO 13485:2003 -Medical devices -Quality management systems -Requirements for regulatory purposes
  20. Fogel, K. (2005). Producing open source software: How to run a successful free software project, O'Reilly Media, Sebastopol
  21. Dawson, B. (2002). Game scripting in Python, Proceedings of the 2002 Game Developers Conference, San Jose
  22. Robinson, S.; Bhatia, V. (1995). Secrets of successful simulation projects, Proceedings of the 1995 Winter Simulation Conference, 61-67
  23. Barlas, P.; Dagkakis, G.; Heavey, C. (2013). A prototype integration of ManPy with CMSD, Proceedings of the 27 th European Simulation and Modelling Conference, 85-90
  24. Dagkakis, G.; Heavey, C.; Robin, S.; Perrin, J. (2013). ManPy: An open-source layer of DES manufacturing objects implemented in SimPy, 8 th EUROSIM Congress on Modelling and Simulation (EUROSIM 2013), 357-363, doi:10.1109/EUROSIM.2013.70
  25. Randell, L. G.; Bolmsjö, G. S. (2001). Database driven factory simulation: a proof-of-concept demonstrator, Proceedings of the 2001 Winter Simulation Conference, 977-983
  26. Ingemansson, A.; Ylipää, T.; Bolmsjö, G. S. (2005). Reducing bottle-necks in a manufacturing system with automatic data collection and discrete-event simulation, Journal of Manufacturing Technology Management, Vol. 16, No. 6, 615-628, doi:10.1108/17410380510609474
  27. Fangohr, H. (2004). A comparison of C, Matlab, and Python as teaching languages in engineering, Bubak, M.; van Albada, G. D.; Sloot, P. M. A.; Dongarra, J. (Eds.). Lecture Notes on Computational Science 3039, ICCS 2004, 1210-1217, Springer-Verlag, Berlin

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