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Safe Approximations of Chance Constraints Using Historical Data

İhsan Yanıkoğlu(ihsan.yanikoglu***at***ozyegin.edu.tr)
Dick den Hertog(d.denhertog***at***uvt.nl)

Abstract: This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach uses the available historical data for the uncertain parameters and is based on goodness-of-fit statistics. It guarantees that the probability that the uncertain constraint holds is at least the prescribed value. Compared to existing safe approximation methods for chance constraints, our approach directly uses the historical-data information and leads to tighter uncertainty sets and therefore to better objective values. This improvement is significant especially when the number of uncertain parameters is low. Other advantages of our approach are that it can handle joint chance constraints easily, it can deal with uncertain parameters that are dependent, and it can be extended to nonlinear inequalities. Several numerical examples illustrate the validity of our approach.

Keywords: robust optimization; chance constraint; phi-divergence; goodness-of-fit statistics

Category 1: Robust Optimization

Category 2: Stochastic Programming

Citation: INFORMS Journal on Computing 25(4): 666--681, 2013

Download: [PDF]

Entry Submitted: 01/11/2015
Entry Accepted: 01/11/2015
Entry Last Modified: 01/11/2015

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