Risk-averse two-stage stochastic programs in furniture plants

Douglas Alem, Reinaldo Morabito

Research output: Contribution to journalArticlepeer-review


We present two-stage stochastic mixed 0-1 optimization models to hedge against uncertainty in production planning of typical small-scale Brazilian furniture plants under stochastic demands and setup times. The proposed models consider cutting and drilling operations as the most limiting production activities, and synchronize them to avoid intermediate work-in-process. To design solutions less sensitive to changes in scenarios, we propose four models that perceive the risk reductions over the scenarios differently. The first model is based on the minimax regret criteria and optimizes a worst-case scenario perspective without needing the probability of the scenarios. The second formulation uses the conditional value-at-risk as the risk measure to avoid solutions influenced by a bad scenario with a low probability. The third strategy is a mean-risk model based on the upper partial mean that aggregates a risk term in the objective function. The last approach is a restricted recourse approach, in which the risk preferences are directly considered in the constraints. Numerical results indicate that it is possible to achieve significant risk reductions using the risk-averse strategies, without overly sacrificing average costs.

Original languageEnglish
Pages (from-to)773-806
Number of pages34
JournalOR Spectrum
Issue number4
Publication statusPublished - Nov 2013


  • conditional value-at-risk
  • furniture industry
  • mean-risk
  • pinimax with regret
  • production planning
  • restricted recourse
  • risk aversion

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