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Interval linear programming under transformations: optimal solutions and optimal value range

Author

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  • Elif Garajová

    (Charles University)

  • Milan Hladík

    (Charles University)

  • Miroslav Rada

    (University of Economics, Prague)

Abstract

Interval linear programming provides a tool for solving real-world optimization problems under interval-valued uncertainty. Instead of approximating or estimating crisp input data, the coefficients of an interval program may perturb independently within the given lower and upper bounds. However, contrarily to classical linear programming, an interval program cannot always be converted into a desired form without affecting its properties, due to the so-called dependency problem. In this paper, we discuss the common transformations used in linear programming, such as imposing non-negativity on free variables or splitting equations into inequalities, and their effects on interval programs. Specifically, we examine changes in the set of all optimal solutions, optimal values and the optimal value range. Since some of the considered properties do not holds in the general case, we also study a special class of interval programs, in which uncertainty only affects the objective function and the right-hand-side vector. For this class, we obtain stronger results.

Suggested Citation

  • Elif Garajová & Milan Hladík & Miroslav Rada, 2019. "Interval linear programming under transformations: optimal solutions and optimal value range," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(3), pages 601-614, September.
    • Handle: RePEc:spr:cejnor:v:27:y:2019:i:3:d:10.1007_s10100-018-0580-5
    DOI: 10.1007/s10100-018-0580-5
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    References listed on IDEAS

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    1. P. Kumar & G. Panda & U.C. Gupta, 2016. "An interval linear programming approach for portfolio selection model," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 27(1/2), pages 149-164.
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    Cited by:

    1. Figueroa–García, Juan Carlos & Hernández, Germán & Franco, Carlos, 2022. "A review on history, trends and perspectives of fuzzy linear programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    2. Milan Hladík, 2023. "Various approaches to multiobjective linear programming problems with interval costs and interval weights," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(3), pages 713-731, September.
    3. Carrabs, Francesco & Cerulli, Raffaele & D’Ambrosio, Ciriaco & Della Croce, Federico & Gentili, Monica, 2021. "An improved heuristic approach for the interval immune transportation problem," Omega, Elsevier, vol. 104(C).
    4. Andrej Kastrin & Janez Povh & Lidija Zadnik Stirn & Janez Žerovnik, 2021. "Methodologies and applications for resilient global development from the aspect of SDI-SOR special issues of CJOR," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 773-790, September.
    5. Jana Novotná & Milan Hladík & Tomáš Masařík, 2020. "Duality Gap in Interval Linear Programming," Journal of Optimization Theory and Applications, Springer, vol. 184(2), pages 565-580, February.

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