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.
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