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). Springer Science and Business Media Deutschland GmbH [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.
paper
Multistage stochastic programming; Scenario-tree generation; Supervised learning;
Stochastic Optimization, Scenario Generation, Stochastic Programming
English
6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020
2020
Archetti, F; Candelieri, A; Galuzzi, B; Messina E
Nicosia G.,Ojha V.,La Malfa E.,Jansen G.,Sciacca V.,Pardalos P.,Giuffrida G.,Umeton R.
6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020
9783030645823
2020
12565
335
346
none
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). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-64583-0_31].
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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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