Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

Prediction Model on Delivery Time in Display FAB Using Survival Analysis

생존분석을 이용한 디스플레이 FAB의 반송시간 예측모형

  • Han, Paul (School of Industrial Management Engineering, Korea University) ;
  • Baek, Jun Geol (School of Industrial Management Engineering, Korea University)
  • 한바울 (고려대학교 산업경영공학과) ;
  • 백준걸 (고려대학교 산업경영공학과)
  • Received : 2013.12.30
  • Accepted : 2014.03.27
  • Published : 2014.06.15
Download PDF
⟨ Previous Next ⟩

Abstract

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Keywords

References

  1. Akaike, H. (1974), A new look at the statistical model identification, Automatic Control, IEEE Transactions on, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
  2. Bradburn, M. J., Clark, T. G., Love, S. B., and Altman, D. G. (2003), Survival analysis Part III : multivariate data analysis-choosing a model and assessing its adequacy and fit, British Journal of Cancer, 89(4), 605. https://doi.org/10.1038/sj.bjc.6601120
  3. Chen, T. (2009), A fuzzy-neural knowledge-based system for job completion time prediction and internal due date assignment in a wafer fabrication plant, International Journal of Systems Science, 40(8), 889-902. https://doi.org/10.1080/00207720902974553
  4. Chung, B. H. and Kim, S. I. (2008), Simulation Model for the Performance Analysis of In-Line Stocker System in TFT-LCD Fabrication Line, Journal of the Korean Institute of Industrial Engineers, 1-10
  5. Cox, D. R. (1972), Regression models and life-tables, Journal of the Royal Statistical Society, Series B (Methodological), 187-220.
  6. Chung, B. Y., Heo, Y. H., Park, M. H., and Jang, S. C. (2010), Classification of the Standby time of Equipment to improve the productivity in TFT-LCD Fab, Journal of the Korean Institute of Industrial Engineers, 1-6.
  7. DeJong, C. D. and Wu, S. P. (2002), Material handling: simulating the transport and scheduling of priority lots in semiconductor factories, In Proceedings of the 34th conference on Winter simulation : exploring new frontiers (1387-1391). Winter Simulation Conference.
  8. Fox, J. (2002), Cox proportional-hazards regression for survival data. Appendix to An R and S-PLUS Companion to Applied Regression. Available at : http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf.
  9. Gu, S. J. and Choi, B. K. (2008), Simulation Model of In-Line Stocker System in TFT-LCD Line, Journal of the Korean Institute of Industrial Engineers, 1-7.
  10. Hosmer Jr, D. W., Lemeshow, S., and May, S. (2011), Applied survival analysis : regression modeling of time to event data, Wiley.com, 618.
  11. Jang, Y. J. and Choi, G. H. (2006), Introduction to automated material handling systems in LCD panel production lines, In Automation Science and Engineering, CASE IEEE International Conference on (223-229), IEEE.
  12. Jenkins, S. P. (2005), Survival analysis, Unpublished manuscript, Institute for Social and Economic Research, University of Essex, Colchester, UK.
  13. Kong, S. H. (2007), Two-step simulation method for automatic material handling system of semiconductor fab, Robotics and Computer- Integrated Manufacturing, 23(4), 409-420. https://doi.org/10.1016/j.rcim.2006.05.004
  14. Lee, S. H., Moon, B. S., and Park, B. J. (2009), The Bus Delay Time Prediction Using Markov Chain, The Journal of The Korea Institute of Intelligent Transport Systems, 8(3), 1-10.
  15. Liao, D. Y. and Wang, C. N. (2004), Neural-network-based delivery time estimates for prioritized 300-mm automatic material handling operations, Semiconductor Manufacturing, IEEE Transactions on, 17(3), 324-332. https://doi.org/10.1109/TSM.2004.831533
  16. Lin, J. S. and Wei, L. J. (1992), Linear regression analysis for multivariate failure time observations, Journal of the American Statistical Association, 87(420), 1091-1097. https://doi.org/10.1080/01621459.1992.10476264
  17. McCulloch, C. E. (2003), Generalized linear mixed models. In NSFCBMS Regional Conference Series in Probability and Statistics (1-84), Institute of Mathematical Statistics and the American Statistical Association.
  18. Qi, J. (2009), Comparison of proportional hazards and accelerated failure time models, Doctoral dissertation, University of Saskatchewan.
  19. Shabtay, D. (2010), Scheduling and due date assignment to minimize earliness, tardiness, holding, due date assignment and batch delivery costs, International Journal of Production Economics, 123(1), 235-242. https://doi.org/10.1016/j.ijpe.2009.08.012
  20. Shiue, Y. R. (2009), Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach, International Journal of Production Research, 47(13), 3669-3690. https://doi.org/10.1080/00207540701846236
  21. Smith, R. M. and Bain, L. J. (1975), An exponential power life-testing distribution, Communications in Statistics-Theory and Methods, 4(5), 469-481.
  22. Son, H. S. and Choi, B. G. (2008), Capacity-Filtering Algorithm based Release Planning Method for TFT-LCD Fab, Journal of the Korean Institute of Industrial Engineers, 817-824.
  23. Wei, L. J. (1992), The accelerated failure time model : a useful alternative to the Cox regression model in survival analysis, Statistics in medicine, 11(14/15), 1871-1879. https://doi.org/10.1002/sim.4780111409