skip to main content
10.5555/1162708.1162757acmconferencesArticle/Chapter ViewAbstractPublication PageswscConference Proceedingsconference-collections
Article

Hybrid discrete event simulation with model predictive control for semiconductor supply-chain manufacturing

Published:04 December 2005Publication History

ABSTRACT

Simulation modeling combined with decision control can offer important benefits for analysis, design, and operation of semiconductor supply-chain network systems. Detailed simulation of physical processes provides information for its controller to account for (expected) stochasticity present in the manufacturing processes. In turn, the controller can provide (near) optimal decisions for the operation of the processes and thus handle uncertainty in customer demands. In this paper, we describe an environment that synthesizes Discrete-EVent System specification (DEVS) with Model Predictive Control (MPC) paradigms using a Knowledge Interchange Broker (KIB). This environment uses the KIB to compose discrete event simulation and model predictive control models. This approach to composability affords flexibility for studying semiconductor supply-chain manufacturing at varying levels of detail. We describe a hybrid DEVS/MPC environments via a knowledge interchange broker. We conclude with a comparison of this work with another that employs the Simulink/MATLAB environment.

References

  1. Davis, P. K. and R. H. Anderson. 2004. Improving the Composability of Department of Defense Models and Simulations. Santa Monica, CA: Rand.Google ScholarGoogle Scholar
  2. DEVSJAVA. 2002. DEVSJAVA Modeling & Simulation Tool. http://www.acims.arizona.edu/SOFTWARE {Accessed February, 2005}.Google ScholarGoogle Scholar
  3. Godding, G. W., H. S. Sarjoughian and K. G. Kempf. 2004. Multi-formalism modeling approach for semiconductor supply/demand networks. Proceedings of the 2004 Winter Simulation Conference, ed. R. G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters, Piscataway, NJ: Institute of Electrical and Electronics Engineers. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Kempf, K. G. 2004. Control-oriented approaches to supply chain management in semiconductor manufacturing. Proceedings of IEEE American Control Conference, Boston, MA.Google ScholarGoogle ScholarCross RefCross Ref
  5. Mathworks. 2002. MATLAB. http://www.mathworks.com/.Google ScholarGoogle Scholar
  6. Mosterman, P. J. and H. Vangheluwe. 2002. Guest editorial: Special issue on computer automated multiparadigm modeling. ACM Transactions on Modeling and Computer Simulation. 12(4): 249--255. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Müller, S. 2002. JMatLink. http://www.held-mueller.de/JMatLink/. {Retrieved November 2004}.Google ScholarGoogle Scholar
  8. Sarjoughian, H. S. and F. E. Cellier, eds. 2001. Discrete Event Modeling and Simulation Technologies: A Tapestry of Systems and AI-Based Theories and Methodologies, Springer Verlag.Google ScholarGoogle Scholar
  9. Sarjoughian, H. S. and J. Plummer. 2002. Design and implementation of a bridge between RAP and DEVS. Tempe, Arizona, Computer Science and Engineering, Arizona State University: 1--26.Google ScholarGoogle Scholar
  10. Singh, R., H. S. Sarjoughian and G. W. Godding. 2004. Design of Scalable Simulation Models for Semiconductor Manufacturing Processes. Summer Computer Simulation Conference, San Jose, CA.Google ScholarGoogle Scholar
  11. Swaminathan, J. M., S. F. Smith and N. M. Sadeh. 1998. Modeling Supply Chain Dynamics: A Multiagent Approach. Decision Sciences, 29(3): 607--632.Google ScholarGoogle ScholarCross RefCross Ref
  12. Vanderbei, R. J. 1999. An interior point code for quadratic programming. Optimization Methods and Software 11:451--484.Google ScholarGoogle ScholarCross RefCross Ref
  13. Wang, W., D. E. Rivera and K. G. Kempf. 2005. A novel model predictive control algorithm for supply chain management in semiconductor manufacturing. American Control Conference, Portland, OR.Google ScholarGoogle Scholar
  14. XML. 2004. eXtensbile Markup Language. http://www.w3.org/XML/.Google ScholarGoogle Scholar
  15. Zeigler, B. P., H. Praehofer and T. G. Kim. 2000. Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems, Academic Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. Hybrid discrete event simulation with model predictive control for semiconductor supply-chain manufacturing

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      WSC '05: Proceedings of the 37th conference on Winter simulation
      December 2005
      2769 pages
      ISBN:0780395190

      Publisher

      Winter Simulation Conference

      Publication History

      • Published: 4 December 2005

      Check for updates

      Qualifiers

      • Article

      Acceptance Rates

      WSC '05 Paper Acceptance Rate209of316submissions,66%Overall Acceptance Rate3,413of5,075submissions,67%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader