A practical assessment of risk-averse approaches in production lot-sizing problems

Douglas Alem, Fabricio Oliveira, Miguel Carrion

Research output: Contribution to journalArticlepeer-review


This paper presents an empirical assessment of four state-of-the-art risk-averse approaches to deal with the capacitated lot-sizing problem under stochastic demand. We analyze two mean-risk models based on the semideviation and on the conditional value-at-risk risk measures, and alternate first and second-order stochastic dominance approaches. The extensive computational experiments based on different instances characteristics and on a case-study suggest that CVaR exhibits a good tradeoff between risk and performance, followed by the semideviation and first-order stochastic dominance approach. For all approaches, enforcing risk-aversion helps to reduce the cost standard deviation substantially, which is usually accomplished via increasing production rates. Overall, we can say that very risk-averse decision-makers would be willing to pay an increased price to have a much less risky solution given by CVaR. In less risk-averse settings, though, semideviation and first-order stochastic dominance can be appealing alternatives to provide significantly more stable production planning costs with a marginal increase of the expected costs
Original languageEnglish
Pages (from-to)2581-2603
JournalInternational Journal of Production Research
Issue number9
Early online date28 May 2019
Publication statusPublished - 1 May 2020


  • lot-sizing
  • two-stage stochastic programming
  • risk-aversion
  • CVaR
  • semideviation
  • first-order stochastic dominance
  • second-order stochastic dominance

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