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A stochastic programming approach for sawmill production planning

Author

Listed:
  • Masoumeh Kazemi Zanjani
  • Daoud Ait-Kadi
  • Mustapha Nourelfath

Abstract

This paper investigates a sawmill production planning problem where the non-homogeneous characteristics of logs result in random process yields. A two-stage stochastic Linear Programming (LP) approach is proposed to address this problem. The random yields are modelled as scenarios with discrete probability distributions. The solution methodology is based on the sample average approximation method. Confidence intervals are constructed for the optimality gap of several candidate solutions, based on Common Random Number (CRN) streams. A computational study including a prototype sawmill is presented to highlight the significance of using the stochastic model instead of the mean-value deterministic model, which is the traditional production planning tool in sawmills.

Suggested Citation

  • Masoumeh Kazemi Zanjani & Daoud Ait-Kadi & Mustapha Nourelfath, 2013. "A stochastic programming approach for sawmill production planning," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 5(1), pages 1-18.
    • Handle: RePEc:ids:ijmore:v:5:y:2013:i:1:p:1-18
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    Cited by:

    1. Venn, Tyron J. & Dorries, Jack W. & McGavin, Robert L., 2021. "A mathematical model to support investment in veneer and LVL manufacturing in subtropical eastern Australia," Forest Policy and Economics, Elsevier, vol. 128(C).
    2. Broz, Diego & Vanzetti, Nicolás & Corsano, Gabriela & Montagna, Jorge M., 2019. "Goal programming application for the decision support in the daily production planning of sawmills," Forest Policy and Economics, Elsevier, vol. 102(C), pages 29-40.
    3. Chia-Nan Wang & Nhat-Luong Nhieu & Trang Thi Thu Tran, 2021. "Stochastic Chebyshev Goal Programming Mixed Integer Linear Model for Sustainable Global Production Planning," Mathematics, MDPI, vol. 9(5), pages 1-22, February.
    4. Paulo Afonso & Vishad Vyas & Ana Antunes & Sérgio Silva & Boris P. J. Bret, 2021. "A Stochastic Approach for Product Costing in Manufacturing Processes," Mathematics, MDPI, vol. 9(18), pages 1-23, September.
    5. Donya Rahmani & Arash Zandi & Sara Behdad & Arezou Entezaminia, 2021. "A light robust model for aggregate production planning with consideration of environmental impacts of machines," Operational Research, Springer, vol. 21(1), pages 273-297, March.

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