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    Robust two-stage stochastic linear optimization with risk aversion

    251411.pdf (424.8Kb)
    Access Status
    Open access
    Authors
    Ling, A.
    Sun, Jie
    Xiu, N.
    Yang, X.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Ling, A. and Sun, J. and Xiu, N. and Yang, X. 2017. Robust two-stage stochastic linear optimization with risk aversion. European Journal of Operational Research. 256 (1): pp. 215-229.
    Source Title
    European Journal of Operational Research
    DOI
    10.1016/j.ejor.2016.06.017
    ISSN
    0377-2217
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/52343
    Collection
    • Curtin Research Publications
    Abstract

    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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