Neural Computing and Applications

, Volume 23, Supplement 1, pp 353–367 | Cite as

A fuzzy-neural approach for supporting three-objective job scheduling in a wafer fabrication factory

Original Article

Abstract

This study is dedicated to three-objective scheduling in a wafer fabrication factory, which has rarely been discussed in the literature but is a very important task. Optimizing a single objective in a complex production system like a wafer fabrication factory is already quite complicated. Optimizing three objectives at the same time is obviously even more complicated. To this end, this study presents a fuzzy-neural approach that fuses three existing rules in a nonlinear way, and which can be tailored, and even optimized, for a wafer fabrication factory. To assess the effectiveness of the proposed methodology, production simulation is also applied in this study. According to the experimental results, the proposed methodology is better than some existing approaches in reducing the average cycle time, the maximum lateness, and cycle time standard deviation.

Keywords

Wafer fabrication Scheduling Fuzzy Neural 

Notes

Acknowledgments

This work was supported by the National Science Council of Taiwan.

References

  1. 1.
    Loukil T, Teghem J, Tuyttens D (2005) Solving multi-objective production scheduling problems using metaheuristics. Eur J Oper Res 161(1):42–61MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Grimme C, Lepping J (2011) Combining basic heuristics for solving multi-objective scheduling problems. In: 2011 IEEE symposium on computational intelligence in scheduling, pp 9–16Google Scholar
  3. 3.
    Chen T (2009) A tailored nonlinear fluctuation smoothing rule for semiconductor manufacturing factory scheduling. Proc Inst Mech Eng Part I J Syst Control Eng 223:149–160CrossRefGoogle Scholar
  4. 4.
    van Wassenhove LN, Gelders F (1980) Solving a bicriterion scheduling problem. Eur J Oper Res 2(4):281–290CrossRefGoogle Scholar
  5. 5.
    Stein C, Wein J (1997) On the existence of schedules that are nearoptimal for both makespan and total weighted completion time. Oper Res Lett 21(3):115–122MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Cochran JK, Horng S-M, Fowler JW (2003) A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Comput Oper Res 30(7):1087–1102MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterMATHGoogle Scholar
  8. 8.
    Coello CAC, Lamont GB, Veldhuizen DAV (2007) Evolutionary algorithms for solving multi-objective problems (Genetic and Evolutionary Computation). Springer, BerlinGoogle Scholar
  9. 9.
    Chen T (2009) Dynamic fuzzy-neural fluctuation smoothing rule for jobs scheduling in a wafer fabrication factory. Proc Inst Mech Eng Part I J Syst Control Eng 223:1081–1094CrossRefGoogle Scholar
  10. 10.
    Chen T (2009) Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication factory: a simulation study. Proc Inst Mech Eng Part B J Eng Manuf 223:1033–1044CrossRefGoogle Scholar
  11. 11.
    Zhang H, Jiang Z, Guo C (2009) Simulation-based optimization of dispatching rules for semiconductor wafer fabrication system scheduling by the response surface methodology. Int J Adv Manuf Technol 41(1–2):110–121CrossRefGoogle Scholar
  12. 12.
    Chen T, Wang YC, Lin YC (2009) A bi-criteria four-factor fluctuation smoothing rule for scheduling jobs in a wafer fabrication factory. Int J Innov Comput Inform Control 6(10):4289–4304MathSciNetGoogle Scholar
  13. 13.
    Chen T, Wang YC, Wu HC (2009) A fuzzy-neural approach for remaining cycle time estimation in a semiconductor manufacturing factory: a simulation study. Int J Innov Comput Inform Control 5(8):2125–2139Google Scholar
  14. 14.
    Chen T, Wang YC (2010) Incorporating the FCM-BPN approach with nonlinear programming for internal due date assignment in a wafer fabrication plant. Robot Comput Integrated Manuf 26:83–91CrossRefGoogle Scholar
  15. 15.
    Pal K, Pal SK (2011) Soft computing methods used for the modelling and optimisation of Gas Metal Arc Welding: a review. Int J Manuf Res 6(1):15–29CrossRefGoogle Scholar
  16. 16.
    Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13:841–847CrossRefGoogle Scholar
  17. 17.
    Nocedal J, Wright SJ (2006) Numerical optimization. Springer, BerlinMATHGoogle Scholar
  18. 18.
    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 45:110–121CrossRefGoogle Scholar
  19. 19.
    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):374–388CrossRefGoogle Scholar
  20. 20.
    Chen T, Wang YC (2009) 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 49:709–721CrossRefGoogle Scholar
  21. 21.
    Chang PC, Hsieh JC, Liao TW (2005) Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory. J Intell Manuf 16:549–557CrossRefGoogle Scholar
  22. 22.
    Chang PC, Liao TW (2006) Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory. Appl Soft Comput 6:198–206CrossRefGoogle Scholar
  23. 23.
    Deif AM, ElMaraghy HA (2011) A multiple performance analysis of market-capacity integration policies. Int J Manuf Res 6(3):191–214CrossRefGoogle Scholar
  24. 24.
    Thomas A, Charpentier P (2005) Reducing simulation models for scheduling manufacturing facilities. Eur J Oper Res 161:111–125CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Industrial Engineering and Systems ManagementFeng Chia UniversityTaichung CityTaiwan 407, ROC

Personalised recommendations