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Distribution-robust loss-averse optimization

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Abstract

Distribution-robust loss-averse optimization optimizes a nominal value with some protection against downside loss, under the assumption that only partial information on the underlying distribution is available. We herein present a general modeling framework for the distribution-robust loss-averse optimization problem. We provide an equivalent simpler formulation that usually permits a tractable solution procedure. We then explore the modeling framework’s relations with traditional robust optimization and mean-variance optimization. Additionally, we discuss extensions to stochastic linear programming.

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Acknowledgments

The authors thank the editor and the anonymous referees for their helpful comments. This research was supported by Research Resettlement Fund for the new faculty of Seoul National University, and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01057719).

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. School of Business, Myongji University, 50-3 Namgajwa-dong, Seodaemun-gu, Seoul, 120-728, Korea

    Kyungchul Park

  2. Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea

    Kyungsik Lee

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  1. Kyungchul Park
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  2. Kyungsik Lee
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Correspondence to Kyungsik Lee.

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Park, K., Lee, K. Distribution-robust loss-averse optimization. Optim Lett 11, 153–163 (2017). https://doi.org/10.1007/s11590-016-1002-z

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  • DOI: https://doi.org/10.1007/s11590-016-1002-z

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