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.
- Davis, P. K. and R. H. Anderson. 2004. Improving the Composability of Department of Defense Models and Simulations. Santa Monica, CA: Rand.Google Scholar
- DEVSJAVA. 2002. DEVSJAVA Modeling & Simulation Tool. http://www.acims.arizona.edu/SOFTWARE {Accessed February, 2005}.Google Scholar
- 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 ScholarDigital Library
- Kempf, K. G. 2004. Control-oriented approaches to supply chain management in semiconductor manufacturing. Proceedings of IEEE American Control Conference, Boston, MA.Google ScholarCross Ref
- Mathworks. 2002. MATLAB. http://www.mathworks.com/.Google Scholar
- 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 ScholarDigital Library
- Müller, S. 2002. JMatLink. http://www.held-mueller.de/JMatLink/. {Retrieved November 2004}.Google Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 ScholarCross Ref
- Vanderbei, R. J. 1999. An interior point code for quadratic programming. Optimization Methods and Software 11:451--484.Google ScholarCross Ref
- 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 Scholar
- XML. 2004. eXtensbile Markup Language. http://www.w3.org/XML/.Google Scholar
- 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 ScholarDigital Library
- Hybrid discrete event simulation with model predictive control for semiconductor supply-chain manufacturing
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