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Solving nonlinear interval optimization problem using stochastic programming technique

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  • P. Kumar

    (Indian Institute of Technology)

  • G. Panda

    (Indian Institute of Technology)

Abstract

In this paper a methodology is developed to solve a nonlinear interval optimization problem by transforming this to a general optimization problem which is free from interval uncertainty. To address the interval uncertainty, relation between an interval and a random variable is established according to the 3 sigma-rule. Using this relation an interval function is associated with a function of random variables and an interval inequality is associated with a chance constraint. The interval optimization problem is then transformed into a nonlinear stochastic programming problem. Further, the existence of a preferable solution of the original problem is established using Chance Constrained Programming technique.

Suggested Citation

  • P. Kumar & G. Panda, 2017. "Solving nonlinear interval optimization problem using stochastic programming technique," OPSEARCH, Springer;Operational Research Society of India, vol. 54(4), pages 752-765, December.
    • Handle: RePEc:spr:opsear:v:54:y:2017:i:4:d:10.1007_s12597-017-0304-y
    DOI: 10.1007/s12597-017-0304-y
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    References listed on IDEAS

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    1. Ishibuchi, Hisao & Tanaka, Hideo, 1990. "Multiobjective programming in optimization of the interval objective function," European Journal of Operational Research, Elsevier, vol. 48(2), pages 219-225, September.
    2. H. C. Wu, 2010. "Duality Theory for Optimization Problems with Interval-Valued Objective Functions," Journal of Optimization Theory and Applications, Springer, vol. 144(3), pages 615-628, March.
    3. Jiang, C. & Han, X. & Liu, G.R. & Liu, G.P., 2008. "A nonlinear interval number programming method for uncertain optimization problems," European Journal of Operational Research, Elsevier, vol. 188(1), pages 1-13, July.
    4. I. M. Stancu-Minasian & M. J. Wets, 1976. "A Research Bibliography in Stochastic Programming, 1955–1975," Operations Research, INFORMS, vol. 24(6), pages 1078-1119, December.
    5. 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.
    6. V. Jeyakumar & G. Y. Li, 2011. "Robust Duality for Fractional Programming Problems with Constraint-Wise Data Uncertainty," Journal of Optimization Theory and Applications, Springer, vol. 151(2), pages 292-303, November.
    7. A. K. Bhurjee & G. Panda, 2016. "Sufficient optimality conditions and duality theory for interval optimization problem," Annals of Operations Research, Springer, vol. 243(1), pages 335-348, August.
    8. A. Bhurjee & G. Panda, 2012. "Efficient solution of interval optimization problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 76(3), pages 273-288, December.
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    Cited by:

    1. P. Kumar & Jyotirmayee Behera & A. K. Bhurjee, 2022. "Solving mean-VaR portfolio selection model with interval-typed random parameter using interval analysis," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 41-77, March.

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