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A variational approach to define robustness for parametric multiobjective optimization problems

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  • Katrin Witting
  • Sina Ober-Blöbaum
  • Michael Dellnitz

Abstract

In contrast to classical optimization problems, in multiobjective optimization several objective functions are considered at the same time. For these problems, the solution is not a single optimum but a set of optimal compromises, the so-called Pareto set. In this work, we consider multiobjective optimization problems that additionally depend on an external parameter $${\lambda \in \mathbb{R}}$$ , so-called parametric multiobjective optimization problems. The solution of such a problem is given by the λ-dependent Pareto set. In this work we give a new definition that allows to characterize λ-robust Pareto points, meaning points which hardly vary under the variation of the parameter λ. To describe this task mathematically, we make use of the classical calculus of variations. A system of differential algebraic equations will turn out to describe λ-robust solutions. For the numerical solution of these equations concepts of the discrete calculus of variations are used. The new robustness concept is illustrated by numerical examples. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Katrin Witting & Sina Ober-Blöbaum & Michael Dellnitz, 2013. "A variational approach to define robustness for parametric multiobjective optimization problems," Journal of Global Optimization, Springer, vol. 57(2), pages 331-345, October.
  • Handle: RePEc:spr:jglopt:v:57:y:2013:i:2:p:331-345
    DOI: 10.1007/s10898-012-9972-6
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    References listed on IDEAS

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    1. JosÉ Figueira & Salvatore Greco & Matthias Ehrogott, 2005. "Multiple Criteria Decision Analysis: State of the Art Surveys," International Series in Operations Research and Management Science, Springer, number 978-0-387-23081-8, December.
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    Cited by:

    1. Chassein, André & Goerigk, Marc, 2018. "Compromise solutions for robust combinatorial optimization with variable-sized uncertainty," European Journal of Operational Research, Elsevier, vol. 269(2), pages 544-555.
    2. Michael Stiglmayr & José Rui Figueira & Kathrin Klamroth & Luís Paquete & Britta Schulze, 2022. "Decision space robustness for multi-objective integer linear programming," Annals of Operations Research, Springer, vol. 319(2), pages 1769-1791, December.
    3. Jonas Ide & Elisabeth Köbis, 2014. "Concepts of efficiency for uncertain multi-objective optimization problems based on set order relations," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 80(1), pages 99-127, August.
    4. Erin K. Doolittle & Hervé L. M. Kerivin & Margaret M. Wiecek, 2018. "Robust multiobjective optimization with application to Internet routing," Annals of Operations Research, Springer, vol. 271(2), pages 487-525, December.
    5. Alexandros Nikas & Angelos Fountoulakis & Aikaterini Forouli & Haris Doukas, 2022. "A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems," Operational Research, Springer, vol. 22(2), pages 1291-1332, April.
    6. Pubudu L. W. Jayasekara & Andrew C. Pangia & Margaret M. Wiecek, 2023. "On solving parametric multiobjective quadratic programs with parameters in general locations," Annals of Operations Research, Springer, vol. 320(1), pages 123-172, January.

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