Robust two-stage stochastic linear optimization with risk aversion

We study a two-stage stochastic linear optimization problem where the recourse function is risk-averse rather than risk neutral. In particular, we consider the mean-conditional value-at-risk objective function in the second stage. The model is robust in the sense that the distribution of the underly...

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Main Authors: Ling, A., Sun, Jie, Xiu, N., Yang, X.
Format: Journal Article
Published: Elsevier BV * North-Holland 2017
Online Access:http://hdl.handle.net/20.500.11937/52343
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author Ling, A.
Sun, Jie
Xiu, N.
Yang, X.
author_facet Ling, A.
Sun, Jie
Xiu, N.
Yang, X.
author_sort Ling, A.
building Curtin Institutional Repository
collection Online Access
description We study a two-stage stochastic linear optimization problem where the recourse function is risk-averse rather than risk neutral. In particular, we consider the mean-conditional value-at-risk objective function in the second stage. The model is robust in the sense that the distribution of the underlying random variable is assumed to belong to a certain family of distributions rather than to be exactly known. We start from analyzing a simple case where uncertainty arises only in the objective function, and then explore the general case where uncertainty also arises in the constraints. We show that the former problem is equivalent to a semidefinite program and the latter problem is generally NP-hard. Applications to two-stage portfolio optimization, material order problems, stochastic production-transportation problem and single facility minimax distance problem are considered. Numerical results show that the proposed robust risk-averse two-stage stochastic programming model can effectively control the risk with solutions of acceptable good quality.
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format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T09:51:25Z
publishDate 2017
publisher Elsevier BV * North-Holland
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spelling curtin-20.500.11937-523432018-07-02T00:45:27Z Robust two-stage stochastic linear optimization with risk aversion Ling, A. Sun, Jie Xiu, N. Yang, X. We study a two-stage stochastic linear optimization problem where the recourse function is risk-averse rather than risk neutral. In particular, we consider the mean-conditional value-at-risk objective function in the second stage. The model is robust in the sense that the distribution of the underlying random variable is assumed to belong to a certain family of distributions rather than to be exactly known. We start from analyzing a simple case where uncertainty arises only in the objective function, and then explore the general case where uncertainty also arises in the constraints. We show that the former problem is equivalent to a semidefinite program and the latter problem is generally NP-hard. Applications to two-stage portfolio optimization, material order problems, stochastic production-transportation problem and single facility minimax distance problem are considered. Numerical results show that the proposed robust risk-averse two-stage stochastic programming model can effectively control the risk with solutions of acceptable good quality. 2017 Journal Article http://hdl.handle.net/20.500.11937/52343 10.1016/j.ejor.2016.06.017 Elsevier BV * North-Holland fulltext
spellingShingle Ling, A.
Sun, Jie
Xiu, N.
Yang, X.
Robust two-stage stochastic linear optimization with risk aversion
title Robust two-stage stochastic linear optimization with risk aversion
title_full Robust two-stage stochastic linear optimization with risk aversion
title_fullStr Robust two-stage stochastic linear optimization with risk aversion
title_full_unstemmed Robust two-stage stochastic linear optimization with risk aversion
title_short Robust two-stage stochastic linear optimization with risk aversion
title_sort robust two-stage stochastic linear optimization with risk aversion
url http://hdl.handle.net/20.500.11937/52343