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.

Archetti, F., Candelieri, A., Galuzzi, B., Messina, V. (2020). Optimal Scenario-Tree Selection for Multistage Stochastic Programming. In 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020 (pp.335-346) [10.1007/978-3-030-64583-0_31].

Optimal Scenario-Tree Selection for Multistage Stochastic Programming

Archetti, F
;
Candelieri, A
;
Galuzzi, B
;
Messina, v
2020

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.
No
paper
Stochastic Optimization, Scenario Generation, Stochastic Programming
English
Machine Learning, Optimization, and Data Science
978-3-030-64582-3
Archetti, F., Candelieri, A., Galuzzi, B., Messina, V. (2020). Optimal Scenario-Tree Selection for Multistage Stochastic Programming. In 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020 (pp.335-346) [10.1007/978-3-030-64583-0_31].
Archetti, F; Candelieri, A; Galuzzi, B; Messina, V
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/298700
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