Uncertainty feature optimization: an implicit paradigm for problems with noisy data

Eggenberg, Niklaus and Salani, Matteo and Bierlaire, Michel (2011) Uncertainty feature optimization: an implicit paradigm for problems with noisy data. Networks, 57 (3). pp. 270-284. ISSN 1097-0037

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Abstract

Optimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise. In this article, we introduce a framework that considers the uncertain data implicitly. We define the concept of Uncertainty Features (UF), which are problem specific structural properties of a solution. We show how to formulate an uncertain problem using the Uncertainty Feature Optimization (UFO) framework as a multi objective problem. We show that stochastic programming and robust optimization are particular cases of the UFO framework. We present computational results for the Multi-Dimensional Knapsack Problem (MDKP) and discuss the application of the framework to the airline scheduling problem.

Item Type:	Scientific journal article, Newspaper article or Magazine article
Subjects:	Mathematical sciences > Operational research
Department/unit:	Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull’intelligenza artificiale USI-SUPSI
Depositing User:	Matteo Salani
Date Deposited:	13 Mar 2014 14:37
Last Modified:	23 May 2016 13:10
URI:	http://repository.supsi.ch/id/eprint/3922

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