Application of Robust Optimization to the Sawmill Planning Problem

Pamela P. Alvarez, Jorge R. Vera

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)457-475
Number of pages19
JournalAnnals of Operations Research
Issue number1
Publication statusPublished - 1 Jan 2014


  • Linear programming
  • Modelling
  • Robust solutions
  • Sawmill production planning
  • Uncertainty

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research


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