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On Relation of Possibly Efficiency and Robust Counterparts in Interval Multiobjective Linear Programming

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Optimization and Decision Science: Methodologies and Applications (ODS 2017)

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Abstract

We investigate multiobjective linear programming with uncertain cost coefficients. We assume that lower and upper bounds for uncertain values are known, no other assumption on uncertain costs is needed. We focus on the so called possibly efficiency, which is defined as efficiency of at least one realization of interval coefficients. We show many favourable properties including existence, low computational performance of determining possibly efficient solutions, convexity of the dominance cone or connectedness or the efficiency set. In the second part, we discuss robust optimization approach for dealing with uncertain costs. We show that the corresponding robust counterpart is closely related to possible efficiency.

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References

  1. Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton University Press (2009)

    Google Scholar 

  2. Bitran, G.R.: Linear multiple objective problems with interval coefficients. Manage. Sci. 26, 694–706 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  3. Charnes, A., Cooper, W.: Management Models and Industrial Applications of Linear Programming. Wiley, New York (1961)

    MATH  Google Scholar 

  4. Ehrgott, M.: Multicriteria Optimization, 2nd edn. Springer, Berlin (2005)

    MATH  Google Scholar 

  5. Gal, T.: Postoptimal Analyses, Parametric Programming, and Related Topics. McGraw-Hill, Hamburg (1979)

    MATH  Google Scholar 

  6. Gal, T., Greenberg, H.J. (eds.): Advances in Sensitivity Analysis and Parametric Programming. Kluwer Academic Publishers, Boston (1997)

    MATH  Google Scholar 

  7. Goberna, M.A., Jeyakumar, V., Li, G., Vicente-Pérez, J.: Robust solutions to multi-objective linear programs with uncertain data. Eur. J. Oper. Res. 242(3), 730–743 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  8. Hladík, M.: Computing the tolerances in multiobjective linear programming. Optim. Methods Softw. 23(5), 731–739 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  9. Hladík, M.: On necessary efficient solutions in interval multiobjective linear programming. In: Antunes, C.H., Insua, D.R., Dias, L.C. (eds.) CD-ROM Proceedings of the 25th Mini-EURO Conference Uncertainty and Robustness in Planning and Decision Making URPDM 15–17 April 2010, Coimbra, Portugal, pp. 1–10 (2010)

    Google Scholar 

  10. Hladík, M.: Complexity of necessary efficiency in interval linear programming and multiobjective linear programming. Optim. Lett. 6(5), 893–899 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  11. Hladík, M.: Weak and strong solvability of interval linear systems of equations and inequalities. Linear Algebra Appl. 438(11), 4156–4165 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  12. Inuiguchi, M., Sakawa, M.: Possible and necessary efficiency in possibilistic multiobjective linear programming problems and possible efficiency test. Fuzzy Sets Syst. 78(2), 231–241 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  13. Nožička, F., Guddat, J., Hollatz, H., Bank, B.: Theorie der Linearen Parametrischen Optimierung. Akademie-Verlag, Berlin (1974)

    MATH  Google Scholar 

  14. Oliveira, C., Antunes, C.H.: Multiple objective linear programming models with interval coefficients—an illustrated overview. Eur. J. Oper. Res. 181(3), 1434–1463 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  15. Rivaz, S., Yaghoobi, M.A.: Some results in interval multiobjective linear programming for recognizing different solutions. Opsearch 52(1), 75–85 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  16. Rivaz, S., Yaghoobi, M.A., Hladík, M.: Using modified maximum regret for finding a necessarily efficient solution in an interval MOLP problem. Fuzzy Optim. Decis. Mak. 15(3), 237–253 (2016)

    Article  MathSciNet  Google Scholar 

  17. Rohn, J.: Solvability of systems of interval linear equations and inequalities. In: Fiedler, M. et al. (eds.) Linear Optimization Problems with Inexact Data, chapter 2, pp. 35–77. Springer, New York (2006)

    Google Scholar 

  18. Wiecek, M.M., Dranichak, G.M.: Robust multiobjective optimization for decision making under uncertainty and conflict. In: Gupta, A., Capponi, A. (eds.) Optimization Challenges in Complex, Networked and Risky Systems, chapter 4, pp. 84–114 (2016) (INFORMS)

    Google Scholar 

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Acknowledgements

The author was supported by the Czech Science Foundation Grant P402/13-10660S.

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Correspondence to Milan Hladík .

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Hladík, M. (2017). On Relation of Possibly Efficiency and Robust Counterparts in Interval Multiobjective Linear Programming. In: Sforza, A., Sterle, C. (eds) Optimization and Decision Science: Methodologies and Applications. ODS 2017. Springer Proceedings in Mathematics & Statistics, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-67308-0_34

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