Efficient Methods for Several Classes of Ambiguous Stochastic Programming Problems under Mean-MAD Information

Krzysztof Postek, Ward Romeijnders, Dick den Hertog, Maartne H. van der Vlerk

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Abstract

We consider decision making problems under uncertainty, assuming that only partial distributional information is available - as is often the case in practice. In such problems, the goal is to determine here-and-now decisions, which optimally balance deterministic immediate costs and worst-case expected future costs. These problems are challenging, since the worst-case distribution needs to be determined while the underlying problem is already a difficult multistage recourse problem. Moreover, as found in many applications, the model may contain integer variables in some or all stages. Applying a well-known result by Ben-Tal and Hochman (1972), we are able to efficiently solve such problems without integer variables, assuming only readily available distributional information on means and mean-absolute deviations. Moreover, we extend the result to the non-convex integer setting by means of convex approximations (see Romeijnders et al. (2016a)), proving corresponding performance bounds.
Our approach is straightforward to implement using of-the-shelf software as illustrated in our numerical experiments.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages48
Volume2016-039
Publication statusPublished - 22 Sep 2016

Publication series

NameCentER Discussion Paper
Volume2016-039

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Keywords

  • robust
  • ambiguous
  • integer
  • recourse
  • stochastic
  • multi-stage

Cite this

Postek, K., Romeijnders, W., den Hertog, D., & van der Vlerk, M. H. (2016). Efficient Methods for Several Classes of Ambiguous Stochastic Programming Problems under Mean-MAD Information. (CentER Discussion Paper; Vol. 2016-039). Tilburg: Operations research.