P2q hierarchical decomposition algorithm for quantile optimization: application to irrigation strategies design

O. Crespo (), J. Bergez () and F. Garcia ()

Annals of Operations Research, 2011, vol. 190, issue 1, 375-387

Abstract: Decision theory dealing with uncertainty is usually considering criteria such as expected, minimum or maximum values. In economic areas, the quantile criterion is commonly used and provides significant advantages. This paper gives interest to the quantile optimization in decision making for designing irrigation strategies. We developed P2q, a hierarchical decomposition algorithm which belongs to the branching methods family. It consists in repeating the creation, evaluation and selection of smaller promising regions. Opposite to common approaches, the main criterion of interest is the α-quantile where α is related to the decision maker risk acceptance. Results of an eight parameters optimization problem are presented. Quantile optimization provided optimal irrigation strategies that differed from thus reached with expected value optimization, responding more accurately to the decision maker preferences. Copyright Springer Science+Business Media, LLC 2011

Keywords: Simulation-based optimization; Quantile optimization; Irrigation management (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s10479-008-0503-2

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