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Well-posedness for the optimistic counterpart of uncertain vector optimization problems

Author

Listed:
  • L. Q. Anh

    (Cantho University)

  • T. Q. Duy

    (FPT University)

  • D. V. Hien

    (University of Science
    Vietnam National University
    Ho Chi Minh City University of Food Industry)

Abstract

In this article, we consider vector optimization problems with uncertain data. We first formulate optimistic counterparts of the reference problems and propose concepts of efficient solutions to such counterparts. We then introduce concepts of pointwise and global well-posedness for optimistic counterparts. Using the generalized Gerstewitz’s function and properties of elements in the image space, we establish the relationships between well-posedness properties for the reference problems and that for scalar optimization ones. Based on such relations, we have studied sufficient conditions of these well-posedness properties for the considered problems via the corresponding scalar problems. Finally, by virtue of a forcing function, the characterizations of the two concepts of well-posedness for such problems are presented.

Suggested Citation

  • L. Q. Anh & T. Q. Duy & D. V. Hien, 2020. "Well-posedness for the optimistic counterpart of uncertain vector optimization problems," Annals of Operations Research, Springer, vol. 295(2), pages 517-533, December.
    • Handle: RePEc:spr:annopr:v:295:y:2020:i:2:d:10.1007_s10479-020-03840-0
    DOI: 10.1007/s10479-020-03840-0
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    References listed on IDEAS

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    1. M. Darabi & J. Zafarani, 2015. "Tykhonov Well-Posedness for Quasi-Equilibrium Problems," Journal of Optimization Theory and Applications, Springer, vol. 165(2), pages 458-479, May.
    2. 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.
    3. 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.
    4. Gábor Kassay, 2010. "On Equilibrium Problems," Springer Optimization and Its Applications, in: Altannar Chinchuluun & Panos M. Pardalos & Rentsen Enkhbat & Ider Tseveendorj (ed.), Optimization and Optimal Control, pages 55-83, Springer.
    5. Giovanni P. Crespi & Daishi Kuroiwa & Matteo Rocca, 2017. "Quasiconvexity of set-valued maps assures well-posedness of robust vector optimization," Annals of Operations Research, Springer, vol. 251(1), pages 89-104, April.
    6. 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.
    7. Caprari, Elisa & Cerboni Baiardi, Lorenzo & Molho, Elena, 2019. "Primal worst and dual best in robust vector optimization," European Journal of Operational Research, Elsevier, vol. 275(3), pages 830-838.
    8. Elodie Adida & Georgia Perakis, 2010. "Dynamic pricing and inventory control: robust vs. stochastic uncertainty models—a computational study," Annals of Operations Research, Springer, vol. 181(1), pages 125-157, December.
    9. Jae Hyoung Lee & Gue Myung Lee, 2018. "On optimality conditions and duality theorems for robust semi-infinite multiobjective optimization problems," Annals of Operations Research, Springer, vol. 269(1), pages 419-438, October.
    10. Jonas Ide & Anita Schöbel, 2016. "Robustness for uncertain multi-objective optimization: a survey and analysis of different concepts," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(1), pages 235-271, January.
    11. Ioana Popescu, 2007. "Robust Mean-Covariance Solutions for Stochastic Optimization," Operations Research, INFORMS, vol. 55(1), pages 98-112, February.
    12. G. P. Crespi & M. Papalia & M. Rocca, 2009. "Extended Well-Posedness of Quasiconvex Vector Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 141(2), pages 285-297, May.
    13. S. Khoshkhabar-amiranloo & E. Khorram, 2015. "Pointwise well-posedness and scalarization in set optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 82(2), pages 195-210, October.
    14. E. Miglierina & E. Molho & M. Rocca, 2005. "Well-Posedness and Scalarization in Vector Optimization," Journal of Optimization Theory and Applications, Springer, vol. 126(2), pages 391-409, August.
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    Cited by:

    1. Kuntal Som & V. Vetrivel, 2023. "Global well-posedness of set-valued optimization with application to uncertain problems," Journal of Global Optimization, Springer, vol. 85(2), pages 511-539, February.

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