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Production planning with uncertainty in the quality of raw materials: a case in sawmills

Author

Listed:
  • M Kazemi Zanjani

    (Université Laval)

  • M Nourelfath

    (Université Laval)

  • D Ait-Kadi

    (Université Laval)

Abstract

Motivated by sawmill production planning, this paper investigates multi-period, multi-product (MPMP) production planning in a manufacturing environment with non-homogeneous raw materials, and consequently random process yields. A two-stage stochastic program with recourse is proposed to address the problem. The random yields are modelled as scenarios with stationary probability distributions during the planning horizon. The solution methodology is based on the sample average approximation (SAA) scheme. The stochastic sawmill production planning model is validated through the Monte Carlo simulation. The computational results for a real medium capacity sawmill highlight the significance of using the stochastic model as a viable tool for production planning instead of the mean-value deterministic model, which is a traditional production planning tool in many sawmills.

Suggested Citation

  • M Kazemi Zanjani & M Nourelfath & D Ait-Kadi, 2011. "Production planning with uncertainty in the quality of raw materials: a case in sawmills," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1334-1343, July.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:7:d:10.1057_jors.2010.30
    DOI: 10.1057/jors.2010.30
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    References listed on IDEAS

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    1. Peter Kall & János Mayer, 2005. "Stochastic Linear Programming," International Series in Operations Research and Management Science, Springer, number 978-0-387-24440-2, December.
    2. S Karabuk, 2008. "Production planning under uncertainty in textile manufacturing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 510-520, April.
    3. Akif Bakir, M. & Byrne, Mike D., 1998. "Stochastic linear optimisation of an MPMP production planning model," International Journal of Production Economics, Elsevier, vol. 55(1), pages 87-96, June.
    4. S C H Leung & Y Wu & K K Lai, 2006. "A stochastic programming approach for multi-site aggregate production planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(2), pages 123-132, February.
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    Cited by:

    1. A. Jamali & E. Khaleghi & I. Gholaminezhad & N. Nariman-Zadeh & B. Gholaminia & A. Jamal-Omidi, 2017. "Multi-objective genetic programming approach for robust modeling of complex manufacturing processes having probabilistic uncertainty in experimental data," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 149-163, January.
    2. Sanei Bajgiran, Omid & Kazemi Zanjani, Masoumeh & Nourelfath, Mustapha, 2016. "The value of integrated tactical planning optimization in the lumber supply chain," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 22-33.

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