Robust optimization approximation for ambiguous P-model and its application
type: Item
instance of: scholarly article
title: Robust optimization approximation for ambiguous P-model and its application
zbMATH Open document ID: 1427.90205
DOI: 10.1155/2018/5203127
author: Ru-Ru Jia
author: Xue-Jie Bai
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Robust optimization is a powerful and relatively novel methodology to cope with optimization problems in the presence of uncertainty. The positive aspect of robust optimization approach is its computational tractability that attracts more and more attention. In this paper, we focus on an ambiguous P-model where probability distributions are partially known. We discuss robust counterpart (RC) of uncertain linear constraints under two refined uncertain sets by robust approach and further find the safe tractable approximations of chance constraints in the ambiguous P-model. Because of the probability constraints embedded in the ambiguous P-model, it is computationally intractable. The advantage of our approach lies in choosing an implicit way to treat stochastic uncertainty models instead of solving them directly. The process above can enable the transformation of proposed P-model to a tractable deterministic one under the refined uncertainty sets. A numerical example about portfolio selection demonstrates that the ambiguous P-model can help the decision maker to determine the optimal investment proportions of various stocks. Sensitivity analyses explore the trade-off between optimization and robustness by adjusting parameter values. Comparison study is conducted to validate the benefit of our ambiguous P-model.
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C31
zbMATH DE Number: 7019096
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/5203127
OpenAlex ID: W2884872981
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Wikidata QID: Q129489768
description: scientific article; zbMATH DE number 7019096