Abstract
Optimization problems are often subject to various kinds of inexactness or inaccuracy of input data. Here, we consider multiobjective linear programming problems, in which two kinds of input entries have the form of interval data. First, we suppose that the objectives entries are interval values, and, second, we suppose that we have an interval estimation of weights of the particular criteria. These two types of interval data naturally lead to various definitions of efficient solutions. We discuss six meaningful concepts of efficient solutions and compare them to each other. For each of them, we attempt to characterize the corresponding kind efficiency and investigate computational complexity of deciding whether a given solution is efficient.



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References
Bitran GR (1980) Linear multiple objective problems with interval coefficients. Manage Sci 26:694–706. https://doi.org/10.1287/mnsc.26.7.694
Dobkin DP, Lipton RJ, Reiss SP (1979) Linear programming is log-space hard for P. Inf Process Lett 8:96–97. https://doi.org/10.1016/0020-0190(79)90152-2
Dranichak GM, Wiecek MM (2019) On highly robust efficient solutions to uncertain multiobjective linear programs. Eur J Oper Res 273(1):20–30. https://doi.org/10.1016/j.ejor.2018.07.035
Ehrgott M (2005) Multicriteria optimization. 2nd ed. Springer, Berlin. https://doi.org/10.1007/3-540-27659-9
Fiedler M, Nedoma J, Ramík J et al (2006) Linear optimization problems with inexact data. Springer, New York. https://doi.org/10.1007/0-387-32698-7
Garajová E, Hladík M, Rada M (2019) Interval linear programming under transformations: optimal solutions and optimal value range. Cent Eur J Oper Res 27(3):601–614. https://doi.org/10.1007/s10100-018-0580-5
Gerlach W (1981) Zur Lösung linearer Ungleichungssysteme bei Störung der rechten Seite und der Koeffizientenmatrix. Math Operationsforsch Stat Ser Optim 12:41–43. https://doi.org/10.1080/02331938108842705
González-Gallardo S, Ruiz AB, Luque M (2021) Analysis of the well-being levels of students in Spain and Finland through interval multiobjective linear programming. Math 9(14). https://doi.org/10.3390/math9141628
Greenlaw R, Hoover HJ, Ruzzo WL (1995) Limits to parallel computation: P-completeness theory. Oxford University Press, New York. https://doi.org/10.1093/oso/9780195085914.001.0001
Hansen ER, Walster GW (2004) Global optimization using interval analysis, 2nd edn. Marcel Dekker, New York. https://doi.org/10.1201/9780203026922
Henriques CO, Inuiguchi M, Luque M et al (2020) New conditions for testing necessarily/possibly efficiency of non-degenerate basic solutions based on the tolerance approach. Eur J Oper Res 283(1):341–355. https://doi.org/10.1016/j.ejor.2019.11.009
Hladík M (2008) Computing the tolerances in multiobjective linear programming. Optim Methods Softw 23(5):731–739. https://doi.org/10.1080/10556780802264204
Hladík M (2012) Complexity of necessary efficiency in interval linear programming and multiobjective linear programming. Optim Lett 6(5):893–899. https://doi.org/10.1007/s11590-011-0315-1
Hladík M (2013) Weak and strong solvability of interval linear systems of equations and inequalities. Linear Algebra Appl 438(11):4156–4165. https://doi.org/10.1016/j.laa.2013.02.012
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, springer proceedings in mathematics & statistics, vol 217. Springer, Cham, pp 335–343. https://doi.org/10.1007/978-3-319-67308-0_34
Hladík M, Sitarz S (2013) Maximal and supremal tolerances in multiobjective linear programming. Eur J Oper Res 228(1):93–101. https://doi.org/10.1016/j.ejor.2013.01.045
Ida M (1996) Generation of efficient solutions for multiobjective linear programming with interval coefficients. In: Proceedings of the SICE Annual Conference SICE’96, Tottori, pp 1041–1044. https://doi.org/10.1109/SICE.1996.865405
Inuiguchi M, Kume Y (1989) A discrimination method of possibly efficient extreme points for interval multiobjective linear programming problems. Trans Soc Instrum Control Eng 25(7):824–826. https://doi.org/10.9746/sicetr1965.25.824
Inuiguchi M, Sakawa M (1996) Possible and necessary efficiency in possibilistic multiobjective linear programming problems and possible efficiency test. Fuzzy Sets Syst 78(2):231–241. https://doi.org/10.1016/0165-0114(95)00169-7
Mohammadi M, Gentili M (2021) The outcome range problem in interval linear programming. Comput Oper Res 129:105–160. https://doi.org/10.1016/j.cor.2020.105160
Moore RE (1966) Interval analysis. Prentice-Hall, Englewood Cliffs
Moore RE, Kearfott RB, Cloud MJ (2009) Introduction to Interval Analysis. SIAM, Philadelphia. https://doi.org/10.1137/1.9780898717716
Mostafaee A, Hladík M (2020) Optimal value bounds in interval fractional linear programming and revenue efficiency measuring. Cent Eur J Oper Res 28(3):963–981. https://doi.org/10.1007/s10100-019-00611-6
Murad A, Al-Ali A, Ellaimony E et al (2010) On bi-criteria two-stage transportation problem: A case study. Transp Probl 5(3):103–114
Neumaier A (1990) Interval methods for systems of equations. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511526473
Nožička F, Grygarová L, Lommatzsch K (1988) Geometrie konvexer Mengen und konvexe Analysis. Akademie-Verlag, Berlin
Oettli W, Prager W (1964) Compatibility of approximate solution of linear equations with given error bounds for coefficients and right-hand sides. Numer Math 6:405–409. https://doi.org/10.1007/BF01386090
Oliveira C, Antunes CH (2007) Multiple objective linear programming models with interval coefficients - an illustrated overview. Eur J Oper Res 181(3):1434–1463. https://doi.org/10.1016/j.ejor.2005.12.042
Rivaz S, Yaghoobi MA (2013) Minimax regret solution to multiobjective linear programming problems with interval objective functions coefficients. Cent Eur J Oper Res 21(3):625–649. https://doi.org/10.1007/s10100-012-0252-9
Rivaz S, Yaghoobi MA (2018) Weighted sum of maximum regrets in an interval MOLP problem. Int Trans Oper Res 25(5):1659–1676. https://doi.org/10.1111/itor.12216
Rivaz S, Yaghoobi MA, Hladík M (2016) Using modified maximum regret for finding a necessarily efficient solution in an interval MOLP problem. Fuzzy Optim Decis Mak 15(3):237–253. https://doi.org/10.1007/s10700-015-9226-4
Rockafellar RT, Wets RJB (2004) Variational Analysis, corr. 2nd print edn. Springer, Berlin. https://doi.org/10.1007/978-3-642-02431-3
Schrijver A (1998) Theory of linear and integer programming. Repr, Wiley, Chichester
The Luc D (2016) Multiobjective linear programming. An introduction. Springer, Cham. https://doi.org/10.1007/978-3-319-21091-9
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The author was supported by the Czech Science Foundation Grant P403-22-11117S.
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Hladík, M. Various approaches to multiobjective linear programming problems with interval costs and interval weights. Cent Eur J Oper Res 31, 713–731 (2023). https://doi.org/10.1007/s10100-022-00804-6
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DOI: https://doi.org/10.1007/s10100-022-00804-6