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

Research output: Contribution to journalArticle


Research units

  • University of Edinburgh
  • Universidad de Castilla-La Mancha


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 analyse 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 trade-off 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
Number of pages32
JournalInternational Journal of Production Research
Publication statusPublished - 28 May 2019
MoE publication typeA1 Journal article-refereed

    Research areas

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

ID: 35243919