Abstract
A distributed and a supervisory scheduling architecture for manufacturing systems and their realization in MATLAB are presented. The distributed architecture uses a set of lower level fuzzy control modules that reduce Work-In- Process (WIP) and synchronize the production system’s operation. The production rate in each stage is controlled so as to satisfy demand, avoid overloading and eliminate machine starvation or blocking. Performance tuning of the distributed controllers has been assigned to a supervisory control architecture. The scheduling objective is to keep the WIP as low as possible maintaining, at the same time, quality of service by keeping backlog into acceptable levels. It is also shown, in this chapter, how MATLAB’s SIMULINK may be used to construct effective production systems simulators. SIMULINK has become very popular in academia and industry and provides a number of powerfull tools, that is almost impossible to find in a dedicated tool for discrete event systems simulation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. B. Gershwin, “Design and operation of manufacturing systems: the controlpoint policy”, IIE Transactions, 32, 891–906 (2000).
J. A. Buzacott and J. G. Shanthikumar, Stochastic Models of Manufacturing Systems (Prentice Hall, Englewood Cliffs, NJ, (1993).
Y. Dallery and G. Liberopoulos, Extended kanban control system: combining kanban and base stock, IIE Transactions on Design and Manufacturing, 32(4), 369–386 (2000).
L. Wein, Scheduling semiconductor wafer fabrication, IEEE Transactions on Semiconductor Manufacturing, 1(3), 115–130 (1988).
R. Akella and P. R. Kumar, Optimal Control of Production Rate in a Failure Prone Manufacturing System, IEEE Transactions on Automatic Control, 312), 116–126 (1986).
T. Bielecki and P. R. Kumar, Optimality of zero-inventory policies for unreliable manufacturing systems, Operations Research, 36(4), 532–541 (1988).
N. Srivatsan, Synthesis of optimal policies for stochastic manufacturing systems, Ph.D. dissertation, Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA., 1993.
G. J. Van Ryzin, S. X. C. Lou and S. B. Gershwin, Production control for a tandem two-machine system, IIE Transactions, 25(5), 5–20 (1993).
S. X. C. Lou, S. P. Sethi and Q. Zhang, Optimal feedback production planning in a stochastic two-machine flowshop, European Journal of Operational Research, 73, 33–345 (1994).
S. X. C. Lou and G. Van Ryzin, Optimal control rules for scheduling job shops, Annals of Operations Research, 17, 233–248 (1989).
S. X. C. Lou and P. Kager, A robust production control policy for VLSI wafer fabrication, IEEE Transactions on Semiconductor Manufacturing, 2, 159–164 (1989).
H. Yan, S. X. C. Lou, S. P. Sethi, A. Gardel and P. Deosthali, Testing the robustness of two-boundary control policies in semiconductor manufacturing, IEEE Transactions on Semiconductor Manufacturing, 9(2), 285–288 (1996).
S. X. Bai and S. B. Gershwin, Scheduling Manufacturing Systems with Work-in-Process Inventory Control: Multiple-Part-Type Systems, International Journal of Production Research, 32(2), 365–386 (1994).
S. X. Bai, S. B. Gershwin, Scheduling Manufacturing Systems with Work-in- Process Inventory Control: Single-Part-Type Systems, IIE Transactions, 27, 599–617 (1995).
L. Custodio, J. Senteiro and C. Bispo, Production Planning and Scheduling using a fuzzy decision system, IEEE Transactions on Robotics and Automation, 10(2), 160–168 (1994).
N. C. Tsourveloudis, E. Dretoulakis and S. Ioannidis, Fuzzy work-in-process inventory control of unreliable manufacturing systems, Information Sciences, 127, 69–83 (2000).
S. Ioannidis, N. Tsourveloudis, K. Valavanis, Fuzzy Supervisory Control of Manufacturing Systems, IEEE Transactions on Robotics and Automation, 20, 379–389 (2004).
H. P. Wiendahl, Load–Oriented Manufacturing Control (Springer-Verlag Hannover, 1995).
D. Driankov, H. Hellendoorn and M. Reinfrank, An Introduction to Fuzzy Control (Springer-Verlag, New York, 1993).
N. Gulley and J.-S. R. Jang, Fuzzy Logic Toolbox User’s Guide (The Math- Works, 1995).
SIMULINK: Dynamic System Simulation for Matlab, (The MathWorks, 1999).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this chapter
Cite this chapter
Ioannidis, S., Tsourveloudis, N. (2006). Fuzzy Techniques in Scheduling of Manufacturing Systems. In: Kahraman, C. (eds) Fuzzy Applications in Industrial Engineering. Studies in Fuzziness and Soft Computing, vol 201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33517-X_18
Download citation
DOI: https://doi.org/10.1007/3-540-33517-X_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33516-0
Online ISBN: 978-3-540-33517-7
eBook Packages: EngineeringEngineering (R0)