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Stochastic programming based capacity planning for semiconductor wafer fab with uncertain demand and capacity

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  • Geng, Na
  • Jiang, Zhibin
  • Chen, Feng

Abstract

Capacity planning is a challenging problem in semiconductor manufacturing industry due to high uncertainties both in market and manufacturing systems, short product life cycle, and expensive capital invest. To tackle this problem, this paper proposes a scenario-based stochastic programming model which considers demand and capacity uncertainties via scenarios, where the overall equipment efficiency is employed to describe the uncertain capacity for the first time. Based on the decentralized structure of tool procurement, production, stockout, and inventory decision-making processes, recourse approximation strategies are presented with varying degree of information share. The computational experiments show that the resulting tool set is robust enough to cope with the changes in capacity with the expected profits being maximized for different scenarios, and the scheme can generate pretty good solutions in reasonable computational time.

Suggested Citation

  • Geng, Na & Jiang, Zhibin & Chen, Feng, 2009. "Stochastic programming based capacity planning for semiconductor wafer fab with uncertain demand and capacity," European Journal of Operational Research, Elsevier, vol. 198(3), pages 899-908, November.
    • Handle: RePEc:eee:ejores:v:198:y:2009:i:3:p:899-908
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    References listed on IDEAS

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    Cited by:

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    3. Martínez-Costa, Carme & Mas-Machuca, Marta & Benedito, Ernest & Corominas, Albert, 2014. "A review of mathematical programming models for strategic capacity planning in manufacturing," International Journal of Production Economics, Elsevier, vol. 153(C), pages 66-85.
    4. Wu, Xiaole & Kouvelis, Panos & Matsuo, Hirofumi & Sano, Hiroki, 2014. "Horizontal coordinating contracts in the semiconductor industry," European Journal of Operational Research, Elsevier, vol. 237(3), pages 887-897.
    5. Chen, Wenliang & Wang, Zheng & Chan, Felix T.S., 2017. "Robust production capacity planning under uncertain wafer lots transfer probabilities for semiconductor automated material handling systems," European Journal of Operational Research, Elsevier, vol. 261(3), pages 929-940.
    6. Tosarkani, Babak Mohamadpour & Amin, Saman Hassanzadeh & Zolfagharinia, Hossein, 2020. "A scenario-based robust possibilistic model for a multi-objective electronic reverse logistics network," International Journal of Production Economics, Elsevier, vol. 224(C).
    7. Kleijnen, Jack P.C. & Pierreval, Henri & Zhang, Jin, 2011. "Methodology for determining the acceptability of system designs in uncertain environments," European Journal of Operational Research, Elsevier, vol. 209(2), pages 176-183, March.
    8. Sabet, Ehsan & Yazdani, Baback & Kian, Ramez & Galanakis, Kostas, 2020. "A strategic and global manufacturing capacity management optimisation model: A Scenario-based multi-stage stochastic programming approach," Omega, Elsevier, vol. 93(C).
    9. Smirnov, Dina & van Jaarsveld, Willem & Atan, Zümbül & de Kok, Ton, 2021. "Long-term resource planning in the high-tech industry: Capacity or inventory?," European Journal of Operational Research, Elsevier, vol. 293(3), pages 926-940.
    10. Lin, James T. & Chen, Tzu-Li & Chu, Hsiao-Ching, 2014. "A stochastic dynamic programming approach for multi-site capacity planning in TFT-LCD manufacturing under demand uncertainty," International Journal of Production Economics, Elsevier, vol. 148(C), pages 21-36.
    11. Sixiang Zhao, 2023. "Decision rule-based method in solving adjustable robust capacity expansion problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 97(2), pages 259-286, April.
    12. de la Torre, R. & Lusa, A. & Mateo, M., 2016. "A MILP model for the long term academic staff size and composition planning in public universities," Omega, Elsevier, vol. 63(C), pages 1-11.

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