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Joint dynamic probabilistic constraints with projected linear decision rules

Vincent Guigues (vguigues***at***fgv.br)
Rene Henrion (henrion***at***wias-berlin.de)

Abstract: We consider multistage stochastic linear optimization problems combining joint dynamic probabilistic constraints with hard constraints. We develop a method for projecting decision rules onto hard constraints of wait-and-see type. We establish the relation between the original (infinite dimensional) problem and approximating problems working with projections from different subclasses of decision policies. Considering the subclass of linear decision rules and a generalized linear model for the underlying stochastic process with noises that are Gaussian or truncated Gaussian, we show that the value and gradient of the objective and constraint functions of the approximating problems can be computed analytically.

Keywords: Dynamic probabilistic constraints; multistage stochastic linear programs; linear decision rules

Category 1: Stochastic Programming

Citation:

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Entry Submitted: 01/20/2016
Entry Accepted: 01/20/2016
Entry Last Modified: 09/15/2016

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