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A Cutting Plane Approach for Chance Constrained Linear Programs

Author

Listed:
  • Andrés Weintraub

    (University of Chile, Santiago, Chile)

  • Jorge Vera

    (University of Chile, Santiago, Chile)

Abstract

One approach for solving linear programs with random coefficients is chance constrained programming. For the case where the technical coefficients are normally distributed, we present a convergent cutting plane algorithm to solve the equivalent nonlinear program, which takes advantage of the characteristics of the problem. The algorithm requires a moderate computational effort and compares favorably with a general nonlinear code and other approaches proposed for solving this problem.

Suggested Citation

  • Andrés Weintraub & Jorge Vera, 1991. "A Cutting Plane Approach for Chance Constrained Linear Programs," Operations Research, INFORMS, vol. 39(5), pages 776-785, October.
    • Handle: RePEc:inm:oropre:v:39:y:1991:i:5:p:776-785
    DOI: 10.1287/opre.39.5.776
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    Citations

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    Cited by:

    1. Zare M., Yahia & Daneshmand, Ahmad, 1995. "A linear approximation method for solving a special class of the chance constrained programming problem," European Journal of Operational Research, Elsevier, vol. 80(1), pages 213-225, January.
    2. Miguel A. Lejeune & Janne Kettunen, 2017. "Managing Reliability and Stability Risks in Forest Harvesting," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 620-638, October.
    3. Poojari, Chandra A. & Varghese, Boby, 2008. "Genetic Algorithm based technique for solving Chance Constrained Problems," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1128-1154, March.
    4. Ran Wei & Alan Murray, 2015. "Spatial uncertainty in harvest scheduling," Annals of Operations Research, Springer, vol. 232(1), pages 275-289, September.
    5. Aslan, Ayse & Ursavas, Evrim & Romeijnders, Ward, 2023. "A Precedence Constrained Knapsack Problem with Uncertain Item Weights for Personalized Learning Systems," Omega, Elsevier, vol. 115(C).
    6. Fernando Veliz & Jean-Paul Watson & Andres Weintraub & Roger Wets & David Woodruff, 2015. "Stochastic optimization models in forest planning: a progressive hedging solution approach," Annals of Operations Research, Springer, vol. 232(1), pages 259-274, September.
    7. Pamela Alvarez & Jorge Vera, 2014. "Application of Robust Optimization to the Sawmill Planning Problem," Annals of Operations Research, Springer, vol. 219(1), pages 457-475, August.
    8. Martell, David L. & Gunn, Eldon A. & Weintraub, Andres, 1998. "Forest management challenges for operational researchers," European Journal of Operational Research, Elsevier, vol. 104(1), pages 1-17, January.

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