Skip to main content
Log in

A bi-criteria nonlinear fluctuation smoothing rule incorporating the SOM–FBPN remaining cycle time estimator for scheduling a wafer fab—a simulation study

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper proposes a bi-criteria nonlinear fluctuation smoothing rule to further improve the performance of job scheduling in a wafer fabrication factory (wafer fab). The rule is based on the well-known fluctuation smoothing rules. First, the remaining cycle time of a job is estimated by applying the self-organization map–fuzzy back propagation network approach to improve the estimation accuracy. Second, two nonlinear forms of the fluctuation smoothing rules are obtained to enhance the balance and responsiveness. Third, the two nonlinear fluctuation smoothing rules are merged into a bi-criteria rule for considering two performance measures (average cycle time and cycle time variation) at the same time. Finally, the content of the bi-criteria rule can be tailored for the wafer fab and be scheduled with an adjustable factor. To evaluate the effectiveness of the proposed methodology, a production simulation was conducted. According to the experimental results, the proposed methodology outperformed some of the existing approaches by reducing the average cycle time and cycle time variation at the same time. In addition, the experimental results showed that the bi-criteria rule made it possible to improve one performance measure without raising the expense of another one.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Wein LM (1998) Scheduling semiconductor wafer fabrication. IEEE Trans Semicond Manuf 1:115–130

    Article  MathSciNet  Google Scholar 

  2. Berry WL, Rao V (1975) Critical ratio scheduling: an experimental analysis. Manag Sci 22(2):192–201

    Article  Google Scholar 

  3. Chen T, Wang YC (2009) A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory. Int J Adv Manuf Technol 45:110–121

    Article  Google Scholar 

  4. Lu SCH, Ramaswamy D, Kumar PR (1994) Efficient scheduling policies to reduce mean and variation of cycle time in semiconductor manufacturing plant. IEEE Trans Semicond Manuf 7(3):374–388

    Article  Google Scholar 

  5. Chen T (2006) A hybrid look-ahead SOM-FBPN and FIR system for wafer lot output time prediction and achievability evaluation. Int J Adv Manuf Technol 35(5–6):575–586

    Google Scholar 

  6. Chen T (2008) A fuzzy–neural approach for estimating the monthly output of a semiconductor manufacturing factory. Int J Adv Manuf Technol 39(5–6):589–598

    Article  Google Scholar 

  7. Chen T, Jeang A, Wang YC (2008) A hybrid neural network and selective allowance approach for internal due date assignment in a wafer fabrication plant. Int J Adv Manuf Technol 36:570–581

    Article  Google Scholar 

  8. Chen T (2008) A SOM-FBPN-ensemble approach with error feedback to adjust classification for wafer-lot completion time prediction. Int J Adv Manuf Technol 37(7–8):782–792

    Article  Google Scholar 

  9. Chen T (2009) A tailored nonlinear fluctuation smoothing rule for semiconductor manufacturing factory scheduling. Proc Inst Mech Eng Part I J Syst Control Eng 223:149–160

    Article  Google Scholar 

  10. Gupta AK, Sivakumar AI (2006) Job shop scheduling techniques in semiconductor manufacturing. Int J Adv Manuf Technol 27:1163–1169

    Article  Google Scholar 

  11. Zhang H, Jiang Z, Guo C (2007) Simulation-based optimization of dispatching rules for semiconductor wafer fabrication system scheduling by the response surface methodology. Int J Adv Manuf Technol. doi:10.2007/s00170-008-1462-0

    Google Scholar 

  12. Li X, Sigurdur O (2004) Data mining for best practices in scheduling data. Proceedings of IIE Annual Conference and Exhibition

  13. Qi C, Sivakumar AI, Gershwin SB (2008) Impact of production control and system factors in semiconductor wafer fabrication. IEEE Trans Semicond Manuf 21(3):376–389

    Article  MATH  Google Scholar 

  14. Rose O (2002) Some issues of the critical ratio dispatch rule in semiconductor manufacturing. Proc of Winter Simul Conf 2:1401–1405

    Google Scholar 

  15. Hsieh B-W, Chen C-H, Chang S-C (2001) Scheduling semiconductor wafer fabrication by using ordinal optimization-based simulation. IEEE Trans Robot Autom 17(5):599–608

    Article  Google Scholar 

  16. Dabbas RM, Chen HN, Fowler JW, Shunk D (2001) A combined dispatching criteria approach to scheduling semiconductor manufacturing systems. Computers and Industrial Engineering 39(3–4):307–324

    Article  Google Scholar 

  17. Dabbas RM, Fowler JW, Rollier DA, McCarville D (2003) Multiple response optimization using mixture-designed experiments and desirability functions in semiconductor scheduling. Int J Prod Res 10(1):939–961

    Article  Google Scholar 

  18. Yoon HJ, Shen W (2008) A multiagent-based decision-making system for semiconductor wafer fabrication with hard temporal constraints. IEEE Trans Semicond Manuf 21(1):83–91

    Article  Google Scholar 

  19. Koonce DA, Tsai S-C (2000) Using data mining to find patterns in genetic algorithm solutions to a job shop schedule. Computers and Industrial Engineering 38(3):361–374

    Article  Google Scholar 

  20. Youssef H, Brigitte C-M, Noureddine Z (2002) A genetic algorithm and data mining based meta-heuristic for job shop scheduling problem. Proc IEEE Int Conf Syst Man Cybern 7:280–285

    Google Scholar 

  21. Hwang T-K, Chang S-C (2003) Design of a Lagrangian relaxation-based hierarchical production scheduling environment for semiconductor wafer fabrication. IEEE Trans Robot Autom 19(4):566–578

    Article  MathSciNet  Google Scholar 

  22. Sourirajan K, Uzsoy R (2007) Hybrid decomposition heuristics for solving large-scale scheduling problems in semiconductor wafer fabrication. J Sched 10(1):41–65

    Article  MATH  Google Scholar 

  23. Chen T, Wu HC (2009) A fuzzy-neural approach for output projection in a semiconductor fabrication factory. Journal of Chinese Institute of Engineers 32(2):287–293

    Google Scholar 

  24. Chen T (2003) A fuzzy back propagation network for output time prediction in a wafer fab. Applied Soft Computing 2(3):211–222

    Article  Google Scholar 

  25. Chen T (2009) A dynamic fuzzy-neural fluctuation smoothing rule for jobs scheduling in a wafer fabrication factory. Proc Inst Mech Eng Part I J Syst Control Eng (in press)

  26. Chou Fuh-Der, Chang PC, Wang Hui-Mei (2008) A simulated annealing approach with probability matrix for semiconductor dynamic scheduling problem. Expert Syst Appl 35(4):1889–1898

  27. Pfund ME, Balasubramanian H, Fowler JW, Mason SJ, Rose O (2008) A multi-criteria approach for scheduling semiconductor wafer fabrication facilities. J Sched 11:29–47

    Article  MATH  Google Scholar 

  28. Zhou MC, Jeng MD (1998) Modeling, analysis, simulation, scheduling, and control of semiconductor manufacturing systems: a Petri net approach. IEEE Trans Semicond Manuf 11(3):333–357

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Toly Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, T., Wang, YC. A bi-criteria nonlinear fluctuation smoothing rule incorporating the SOM–FBPN remaining cycle time estimator for scheduling a wafer fab—a simulation study. Int J Adv Manuf Technol 49, 709–721 (2010). https://doi.org/10.1007/s00170-009-2424-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-009-2424-x

Keywords

Navigation