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Bounds in multi-horizon stochastic programs

Author

Listed:
  • Francesca Maggioni

    (University of Bergamo)

  • Elisabetta Allevi

    (Brescia University)

  • Asgeir Tomasgard

    (Norwegian University of Science and Technology)

Abstract

In this paper, we present bounds for multi-horizon stochastic optimization problems, a class of problems introduced in Kaut et al. (Comput Manag Sci 11:179–193, 2014) relevant in many industry-life applications typically involving strategic and operational decisions on two different time scales. After providing three general mathematical formulations of a multi-horizon stochastic program, we extend the definition of the traditional Expected Value problem and Wait-and-See problem from stochastic programming in a multi-horizon framework. New measures are introduced allowing to quantify the importance of the uncertainty at both strategic and operational levels. Relations among the solution approaches are then determined and chain of inequalities provided. Numerical experiments based on an energy planning application are finally presented.

Suggested Citation

  • Francesca Maggioni & Elisabetta Allevi & Asgeir Tomasgard, 2020. "Bounds in multi-horizon stochastic programs," Annals of Operations Research, Springer, vol. 292(2), pages 605-625, September.
    • Handle: RePEc:spr:annopr:v:292:y:2020:i:2:d:10.1007_s10479-019-03244-9
    DOI: 10.1007/s10479-019-03244-9
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    References listed on IDEAS

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    Cited by:

    1. Castro, Jordi & Escudero, Laureano F. & Monge, Juan F., 2023. "On solving large-scale multistage stochastic optimization problems with a new specialized interior-point approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 268-285.
    2. Guo, Peijun, 2022. "Dynamic focus programming: A new approach to sequential decision problems under uncertainty," European Journal of Operational Research, Elsevier, vol. 303(1), pages 328-336.

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