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International Conference on Machine Learning, Optimization, and Data Science

LOD 2020: Machine Learning, Optimization, and Data Science pp 335–346Cite as

Optimal Scenario-Tree Selection for Multistage Stochastic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12565))

Abstract

We propose an algorithmic strategy for Multistage Stochastic Optimization, to learn a decision policy able to provide feasible and optimal decisions for every possible value of the random variables of the problem. The decision policy is built using a scenario-tree based solution combined with a regression model able to provide a decision also for those scenarios not included in the tree. For building an optimal policy, an iterative scenario generation procedure is used which selects through a Bayesian Optimization process the more informative scenario-tree. Some preliminary numerical tests show the validity of such an approach.

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Notes

  1. 1.

    https://www.aimms.com/english/developers/resources/examples/functional-examples/stochastic-programming/.

  2. 2.

    http://www.gurobi.com.

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Author information

Authors and Affiliations

  1. University of Milano-Bicocca, 20125, Milan, Italy

    Bruno G. Galuzzi, Enza Messina, Antonio Candelieri & Francesco Archetti

  2. Consorzio Milano-Ricerche, via Roberto Cozzi, 53, 20126, Milan, Italy

    Francesco Archetti

Authors
  1. Bruno G. Galuzzi
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  2. Enza Messina
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  3. Antonio Candelieri
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  4. Francesco Archetti
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Corresponding author

Correspondence to Bruno G. Galuzzi .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Galuzzi, B.G., Messina, E., Candelieri, A., Archetti, F. (2020). Optimal Scenario-Tree Selection for Multistage Stochastic Programming. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_31

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  • DOI: https://doi.org/10.1007/978-3-030-64583-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64582-3

  • Online ISBN: 978-3-030-64583-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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