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Robust Discrete Optimization Under Discrete and Interval Uncertainty: A Survey

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Robustness Analysis in Decision Aiding, Optimization, and Analytics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 241))

Abstract

In this chapter a review of recent results on robust discrete optimization is presented. The most popular discrete and interval uncertainty representations are discussed. Various robust concepts are presented, namely the traditional minmax (regret) approach with some of its recent extensions, and several two-stage concepts. A special attention is paid to the computational properties of the robust problems considered.

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Acknowledgements

This work was partially supported by the National Center for Science (Narodowe Centrum Nauki), grant 2013/09/B/ST6/01525.

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Authors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland

    Adam Kasperski

  2. Faculty of Fundamental Problems of Technology, Wrocław University of Technology, Wrocław, Poland

    Paweł Zieliński

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  1. Adam Kasperski
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Correspondence to Paweł Zieliński .

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  1. School of Production Engineering & Mgmt, Technical University of Crete, Chania, Greece

    Michael Doumpos

  2. School of Production Engineering & Mgmt, Technical University of Crete, Chania, Greece

    Constantin Zopounidis

  3. School of Production Engineering & Mgmt, Technical University of Crete, Chania, Greece

    Evangelos Grigoroudis

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Kasperski, A., Zieliński, P. (2016). Robust Discrete Optimization Under Discrete and Interval Uncertainty: A Survey. In: Doumpos, M., Zopounidis, C., Grigoroudis, E. (eds) Robustness Analysis in Decision Aiding, Optimization, and Analytics. International Series in Operations Research & Management Science, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-33121-8_6

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