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Compacting multistage stochastic programming models through a new implicit extensive framework
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DOI：10.1016/j.jocs.2020.101128.png)
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Abstract
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Deterministic equivalent models reformulate optimization problems from a computational perspective. Nonetheless, these models become computationally intractable quickly when the number of stages increase. In this context, a framework to reduce the size of scenario tree and multistage stochastic optimization problems is proposed. Scenario trees are generated using the Knuth transformation for a more compact representation. Moreover, the optimization model is described by using an implicit extensive form approach. The framework is tested in an asset-liability management multistage stochastic model with joint chance constraints, making it possible to acquire the optimal solution for large instances without any relaxation or decomposition mechanism. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Stochastic programming
Implicit deterministic equivalent
Algebraic language modeling
Multistage
Scenario tree
Knuth natural correspondence
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