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Concepts of efficiency for uncertain multi-objective optimization problems based on set order relations

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  • Jonas Ide
  • Elisabeth Köbis

Abstract

In this paper we present new concepts of efficiency for uncertain multi-objective optimization problems. We analyze the connection between the concept of minmax robust efficiency presented by Ehrgott et al. (Eur J Oper Res, 2014 , doi: 10.1016/j.ejor.2014.03.013 ) and the upper set less order relation $$\preceq _s^u$$ ⪯ s u introduced by Kuroiwa ( 1998 , 1999 ). From this connection we derive new concepts of efficiency for uncertain multi-objective optimization problems by replacing the set ordering with other set orderings. Those are namely the lower set less ordering (see Kuroiwa 1998 , 1999 ), the set less ordering (see Nishnianidze in Soobshch Akad Nauk Gruzin SSR 114(3):489–491, 1984 ; Young in Math Ann 104(1):260–290, 1931 , doi: 10.1007/BF01457934 ; Eichfelder and Jahn in Vector Optimization. Springer, Berlin, 2012 ), the certainly less ordering (see Eichfelder and Jahn in Vector Optimization. Springer, Berlin, 2012 ), and the alternative set less ordering (see Ide et al. in Fixed Point Theory Appl, 2014 , doi: 10.1186/1687-1812-2014-83 ; Köbis 2014 ). We analyze the resulting concepts of efficiency and present numerical results on the occurrence of the various concepts. We conclude the paper with a short comparison between the concepts, and an outlook to further work. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • 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.
  • Handle: RePEc:spr:mathme:v:80:y:2014:i:1:p:99-127
    DOI: 10.1007/s00186-014-0471-z
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    References listed on IDEAS

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    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. F. Guerriero & R. Musmanno, 2001. "Label Correcting Methods to Solve Multicriteria Shortest Path Problems," Journal of Optimization Theory and Applications, Springer, vol. 111(3), pages 589-613, December.
    3. 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.
    4. Ehrgott, Matthias & Ide, Jonas & Schöbel, Anita, 2014. "Minmax robustness for multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 239(1), pages 17-31.
    5. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    6. H. Yu & H. M. Liu, 2013. "Robust Multiple Objective Game Theory," Journal of Optimization Theory and Applications, Springer, vol. 159(1), pages 272-280, October.
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