Worst-Case-Expectation Approach to Optimization Under Uncertainty

Alexander Shapiro (), Wajdi Tekaya (), Murilo Pereira Soares () and Joari Paulo da Costa ()
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Alexander Shapiro: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Wajdi Tekaya: Cambridge Systems Associates, Cambridge CB5 8AF, United Kingdom
Murilo Pereira Soares: Operador Nacional do Sistema Elétrico, Rio de Janeiro, RJ 20211-160, Brazil
Joari Paulo da Costa: Operador Nacional do Sistema Elétrico, Rio de Janeiro, RJ 20211-160, Brazil

Operations Research, 2013, vol. 61, issue 6, 1435-1449

Abstract: In this paper we discuss multistage programming with the data process subject to uncertainty. We consider a situation where the data process can be naturally separated into two components: one can be modeled as a random process, with a specified probability distribution, and the other one can be treated from a robust (worst-case) point of view. We formulate this in a time consistent way and derive the corresponding dynamic programming equations. To solve the obtained multistage problem, we develop a variant of the stochastic dual dynamic programming method. We give a general description of the algorithm and present computational studies related to planning of the Brazilian interconnected power system.

Keywords: multistage stochastic programming; robust optimization; time consistency; dynamic equations; stochastic dual dynamic programming; sample average approximation; risk-neutral and risk-averse approaches; case studies (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:61:y:2013:i:6:p:1435-1449

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