Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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A Path Specification Approach for Production Planning in Semiconductor Industry

  • Received : 2010.11.29
  • Accepted : 2010.12.17
  • Published : 2010.12.31
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Abstract

This paper explores a new approach for modeling of decision-making problems that involve uncertain, time-dependent and sequence-dependent processes which can be applied to semiconductor industry. In the proposed approach, which is based on probability theory, approximate sample paths are required to be specified by probability and statistic characteristics. Completely specified sample paths are seen to be elementary and fundamental outcomes of the related experiment. The proposed approach is suitable for modeling real processes more accurately. A case study is applied to a single item production planning problem with continuous and uncertain demand and the solution obtained by the approximate path specification method shows less computational efforts and practically desirable features. The application possibility and general plan of the proposed approach in semiconductor manufacturing process is also described in the paper.

Keywords

References

  1. Bather, J., "A continuous review model," Journal of Applied Probability. Vol.3, pp. 538-549, 1966. https://doi.org/10.2307/3212137
  2. Sulem, A., "A solvable one-dimensional model of a diffusion inventory system," Mathematics of Operations Research, Vol. 11, pp. 125-133, 2004.
  3. Berman, O., Perry, D., Stadje, W., "Optimal replenishment in Brownian motion EOQ model with hysteric parameter changes," International Journal of Inventory Research, Vol. 1(1), pp. 1-19, 2008. https://doi.org/10.1504/IJIR.2008.019205
  4. Ryan, S. M., "Capacity expansion for random exponential demand growth with lead times," Management Science, Vol. 50(6), pp. 740-748, 2004. https://doi.org/10.1287/mnsc.1030.0187
  5. Marathe, R. R., Ryan, S. M., "Capacity expansion under a service-level constraint for uncertain demands with lead times," Naval Research Logistics, Vol. 56(3) pp.250-263, 2009. https://doi.org/10.1002/nav.20334
  6. Jagannathan, R., "Linear programming with stochastic processes as parameters as applied to production planning," Annals of Operations Research, Vol. 30(1), pp. 107-114, 1991. https://doi.org/10.1007/BF02204812
  7. Szmerekovsky, J. G., "Single machine scheduling under market uncertainty," European Journal of Operational Research, Vol. 177(1), pp. 163-175, 2007. https://doi.org/10.1016/j.ejor.2005.09.047
  8. Kanniainen, J., "Can properly discounted projects follow geometric Brownian motion?," Mathematical Methods of Operations Research, Vol. 70(3), pp. 435-450, 2009. https://doi.org/10.1007/s00186-008-0275-0
  9. Zimmermann, J. H., Fuzzy Theory and Its Applications, 4th ed. Kluwer, Boston, 2001.
  10. Mula, J., Poler, R., Garcia-Sabater, J. P., Lario, F. C., "Models for production planning under uncertainty: a review," International Journal of Production Economics, Vol.103, pp. 271-285, 1999.
  11. Bitran, G., Yanesse, H. H., "Deterministic approximation to stochastic production problems," Operations Research, Vol. 32, pp. 999-1018, 1984. https://doi.org/10.1287/opre.32.5.999
  12. Mujde, E., "Analysis of production planning in case of the random demand," Computers & Industrial Engineering, Vol. 37 (1-2), pp.21-25, 1999. https://doi.org/10.1016/S0360-8352(99)00015-7
  13. Jamalnia, A., Soukhakian, M. A., "A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning," Computers & Industrial Engineering, Vol. 56(4), pp.1474-1486, 2009. https://doi.org/10.1016/j.cie.2008.09.010
  14. Uchino, E. and Yamakawa, T., "System modeling by a neo-fuzzy-neuron with applications to acoustic and chaotic systems," International Journal of Artificial Intelligence Tools, Vol. 4 (1 & 2), pp. 73-91. 1995. https://doi.org/10.1142/S021821309500005X