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Application of Robust Optimization to the Sawmill Planning Problem

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  • Pamela Alvarez
  • Jorge Vera

Abstract

Optimization models have been used to support decision making in the forest industry for a long time. However, several of those models are deterministic and do not address the variability that is present in some of the data. Robust Optimization is a methodology which can deal with the uncertainty or variability in optimization problems by computing a solution which is feasible for all possible scenarios of the data within a given uncertainty set. This paper presents the application of the Robust Optimization Methodology to a Sawmill Planning Problem. In the particular case of this problem, variability is assumed in the yield coefficients associated to the cutting patterns used. The main results show that the loss in the function objective value (the “Price of Robustness”), due to computing robust solutions, is not excessive. Moreover, the computed solutions remain feasible for a large proportion of randomly generated scenarios, and tend to preserve the structure of the nominal solution. We believe that these results provide an application area for Robust Optimization in which several source of uncertainty are present. Copyright Springer Science+Business Media, LLC 2014

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  • Pamela Alvarez & Jorge Vera, 2014. "Application of Robust Optimization to the Sawmill Planning Problem," Annals of Operations Research, Springer, vol. 219(1), pages 457-475, August.
    • Handle: RePEc:spr:annopr:v:219:y:2014:i:1:p:457-475:10.1007/s10479-011-1002-4
    DOI: 10.1007/s10479-011-1002-4
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    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. Yarong Chen & Hongming Zhou & Peiyu Huang & FuhDer Chou & Shenquan Huang, 2022. "A refined order release method for achieving robustness of non-repetitive dynamic manufacturing system performance," Annals of Operations Research, Springer, vol. 311(1), pages 65-79, April.
    4. Xide Zhu & Peijun Guo, 2020. "Bilevel programming approaches to production planning for multiple products with short life cycles," 4OR, Springer, vol. 18(2), pages 151-175, June.
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    6. Vanzetti, Nicolás & Broz, Diego & Corsano, Gabriela & Montagna, Jorge M., 2018. "An optimization approach for multiperiod production planning in a sawmill," Forest Policy and Economics, Elsevier, vol. 97(C), pages 1-8.
    7. Santos, Maria João & Curcio, Eduardo & Mulati, Mauro Henrique & Amorim, Pedro & Miyazawa, Flávio Keidi, 2020. "A robust optimization approach for the vehicle routing problem with selective backhauls," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).

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