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Adjustable Robust Optimization Models for Nonlinear Multi-Period Optimization

Akiko Takeda (takeda***at***is.titech.ac.jp)
Shunsuke Taguchi (taguchi0***at***is.titech.ac.jp)
Reha H. Tutuncu (reha***at***cmu.edu)

Abstract: We study multi-period nonlinear optimization problems whose parameters are uncertain. We assume that uncertain parameters are revealed in stages and model them using the adjustable robust optimization approach. For problems with polytopic uncertainty, we show that quasi-convexity of the optimal value function of certain subproblems is sufficient for the reducibility of the resulting robust optimization problem to a single-level deterministic problem. We relate this sufficient condition to the quasi cone-convexity of the feasible set mapping for adjustable variables and provide several examples satisfying these conditions.

Keywords: robust optimization, quasi-convexity, multi-period optimization

Category 1: Robust Optimization

Citation: Research Report No. 04-CNA-013, Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA, August 2004

Download: [PDF]

Entry Submitted: 08/17/2004
Entry Accepted: 08/17/2004
Entry Last Modified: 08/17/2004

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