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Interactive Multiobjective Robust Optimization with NIMBUS

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Simulation Science (SimScience 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 889))

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Abstract

In this paper, we introduce the MuRO-NIMBUS method for solving multiobjective optimization problems with uncertain parameters. The concept of set-based minmax robust Pareto optimality is utilized to tackle the uncertainty in the problems. We separate the solution process into two stages: the pre-decision making stage and the decision making stage. We consider the decision maker’s preferences in the nominal case, i.e., with the most typical or undisturbed values of the uncertain parameters. At the same time, the decision maker is informed about the objective function values in the worst case to support her/him to make an informed decision. To help the decision maker to understand the behaviors of the solutions, we visually present the objective function values. As a result, the decision maker can find a preferred balance between robustness and objective function values under the nominal case.

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Authors and Affiliations

  1. University of Jyvaskyla, Faculty of Information Technology, P.O.Box 35, Agora, 40014, University of Jyvaskyla, Finland

    Yue Zhou-Kangas, Kaisa Miettinen & Karthik Sindhya

Authors
  1. Yue Zhou-Kangas
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  2. Kaisa Miettinen
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  3. Karthik Sindhya
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Corresponding author

Correspondence to Yue Zhou-Kangas .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Göttingen, Göttingen, Lower Saxony, Germany

    Marcus Baum

  2. Institute of Applied Mechanics, TU Clausthal, Clausthal-Zellerfeld, Lower Saxony, Germany

    Gunther Brenner

  3. Institute of Computer Science, Universität Göttingen, Göttingen, Lower Saxony, Germany

    Jens Grabowski

  4. Institute of Applied Stochastics and Operations Research, TU Clausthal, Clausthal-Zellerfeld, Lower Saxony, Germany

    Thomas Hanschke

  5. Institute of Applied Mechanics, TU Clausthal, Clausthal-Zellerfeld, Lower Saxony, Germany

    Stefan Hartmann

  6. Institute for Numerical and Applied Mathematics, Universität Göttingen, Göttingen, Lower Saxony, Germany

    Anita Schöbel

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Zhou-Kangas, Y., Miettinen, K., Sindhya, K. (2018). Interactive Multiobjective Robust Optimization with NIMBUS. In: Baum, M., Brenner, G., Grabowski, J., Hanschke, T., Hartmann, S., Schöbel, A. (eds) Simulation Science. SimScience 2017. Communications in Computer and Information Science, vol 889. Springer, Cham. https://doi.org/10.1007/978-3-319-96271-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-96271-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96270-2

  • Online ISBN: 978-3-319-96271-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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