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What is Two-Stage Robust or Stochastic Optimization

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics
This refers to an optimization problem with uncertain or random parameters, in which the set of decision variables (x, y) is decomposed into two distinct subsets: the set of primary (or 'here and now') decision variables x, and the set of secondary (or 'wait-and-see') variables y; the former have to be fixed prior to getting any information about the realization of the uncertain or random parameters, while the latter can be adjusted after observing the realized values of the uncertain or random parameters.
Published in Chapter:
Robust Two-Stage and Multistage Optimization: Complexity Issues and Applications
Michel Andre Minoux (University P. and M. Curie, France)
DOI: 10.4018/978-1-4666-9644-0.ch002
Abstract
This chapter is intended as an overview of robust optimization models related to optimization problems subject to uncertain data, with special focus on the case when uncertainty impacts the right-hand side coefficients in the constraints. Two-stage as well as multistage models are addressed, emphasizing links with applications and computational complexity issues. A class of multistage robust optimization problems for which exact optimal strategies can be efficiently computed (via a robust dynamic programming recursion) is discussed. An application to a multiperiod energy production planning problem is presented into detail, and computational results are reported.
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