Worst-case-expectation approach to optimization under uncertainty

Published: 2012/10/30

Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming case studies, dynamic equations, multistage stochastic programming, risk neutral and risk averse approaches, robust distribution stochastic programming, robust optimization, sample average approximation, stochastic dual dynamic programming, time consistency Short URL: https://optimization-online.org/?p=12213

In this paper we discuss multistage programming with the data process subject to uncertainty. We consider a situation were 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. In order to solve the obtained multistage problem we develop a variant of the Stochastic Dual Dynamic Programming (SDDP) method. We give a general description of the algorithm and present computational studies related to planning of the Brazilian interconnected power system.

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