Assessing policy quality in a multistage stochastic program for long-term hydrothermal scheduling

Vitor L. de Matos, David P. Morton*, Erlon C. Finardi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


We consider a multistage stochastic linear program in which we aim to assess the quality of an operational policy computed by means of a stochastic dual dynamic programming algorithm. We perform policy assessment by considering two strategies to compute a confidence interval on the optimality gap: (i) using multiple scenario trees and (ii) using a single scenario tree. The first approach has already been considered in several applications, while the second approach has been discussed previously only in a two-stage framework. The second approach is useful in practical applications in order to more quickly assess the quality of a policy. We present these ideas in the context of a multistage stochastic program for Brazilian long-term hydrothermal scheduling, and use numerical instances to compare the confidence intervals on the optimality gap computed via both strategies. We further consider the relative merits of using naive Monte Carlo sampling, randomized quasi Monte Carlo sampling, and Latin hypercube sampling within our framework for assessing the quality of a policy.

Original languageEnglish (US)
Pages (from-to)713-731
Number of pages19
JournalAnnals of Operations Research
Issue number2
StatePublished - Jun 1 2017


  • Hydrothermal scheduling
  • Stochastic dual dynamic programming
  • Stochastic programming

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research


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