Risk-averse feasible policies for large-scale multistage stochastic linear programs

Resumo

We consider risk-averse formulations of stochastic linear programs having a structure that is common in real-life applications. Specifically, the optimization problem corresponds to controlling over a certain horizon a system whose dynamics is given by a transition equation depending affinely on an interstage dependent stochastic process. We put in place a rolling-horizon time consistent policy. For each time step, a risk-averse problem with constraints that are deterministic for the current time step and uncertain for future times is solved. To each uncertain constraint corresponds both a chance and a Conditional Value-at-Risk constraint. We show that the resulting risk-averse problems are numerically tractable, being at worst conic quadratic programs. For the particular case in which uncertainty appears only on the right-hand side of the constraints, such risk-averse problems are linear programs. We show how to write dynamic programming equations for these problems and define robust recourse functions that can be approximated recursively by cutting planes. The methodology is assessed and favourably compared with Stochastic Dual Dynamic Programming on a real size water-resource planning problem.

Ficha do documento

Tipo

Artigo científico

Ano

2013

Instituição

Springer Heidelberg

Fonte

Repositório da FGV

Idioma

Inglês

Acesso

Acesso restrito

DOI

10.1007/s10107-012-0592-1

Identificador

oai:repositorio.fgv.br:10438/23310

Temas

Tecnologia

Palavras-chave

Stochastic programmingChance constraintsCVaRInterstage dependenceDynamic programmingTecnologiaProcesso estocástico - Programas de computador