A branch and bound method for stochastic global optimization (original) (raw)

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. Yu.M. Ermoliev, Stochastic quasi-gradient methods and their application to systems optimization, Stochastics 4 (1983) 1–37.
    MathSciNet Google Scholar
  2. J.R. Birge, Decomposition and partitioning methods for multistage stochastic linear programs, Operations Research 33 (1985) 989–1007.
    MATH MathSciNet Google Scholar
  3. J.M. Mulvey, A. Ruszczyński, A new scenario decomposition method for large-scale stochastic optimization. Operations Research 43 (1995) 477–490.
    Article MATH MathSciNet Google Scholar
  4. R.T. Rockafellar, R.J.-B. Wets, Scenarios and policy aggregation in optimization under uncertainty, Mathematics of Operations Research 16 (1991) 1–23.
    MathSciNet Google Scholar
  5. R.J.-B. Wets, Large scale linear programming techniques, in: Yu. Ermoliev, R.J.-B. Wets (Eds.), Numerical Methods in Stochastic Programming, Springer, Berlin, 1988, pp. 65–94.
    Google Scholar
  6. V.S. Mikhalevich, A.M. Gupal, V.I. Norkin, Methods of Non-Convex Optimization, Nauka, Moscow, 1987 (in Russian).
    MATH Google Scholar
  7. V.I. Norkin, Yu.M. Ermoliev, A. Ruszczyński, On optimal allocation of indivisibles under uncertainty, Operations Research to appear in 1998.
  8. R. Horst, P.M. Pardalos (Eds.), Handbook of Global Optimization, Kluwer Academic Publishers, Dordrecht, 1994.
    Google Scholar
  9. R.Y. Rubinstein, A. Shapiro, Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method, Wiley, New York, 1993.
    MATH Google Scholar
  10. V.I. Norkin, The analysis and optimization of probability functions, Working Paper WP-93-6, International Institute of Applied System Analysis, Laxenburg, Austria, 1993.
    Google Scholar
  11. A. Prekopa, Logarithmic concave measures and related topics, in: M.A.H. Dempster (Ed.), Stochastic Programming, Academic Press, London, pp. 63–82.
  12. P. Hansen, B. Jaumard, H. Tuy, Global optimization in location, in: Z. Drezner (Ed.), Facility Location—A Survey of Applications and Methods, Springer Series in Operations Research, Springer, Berlin, pp. 43–68.
  13. M. Labbè, D. Peeters, J.-F. Thisse, Location on networks, in: M.O. Ball (Ed.), Handbooks in OR & MS, vol. 8, ch. 7, Elsevier, Amsterdam, pp. 551–624.
  14. B.J. Lence, A. Ruszczyński, Managing water quality under uncertainty: application of a new stochastic branch and bound method, Working Paper WP-96-66, International Institute for Applied System Analysis, Laxenburg, Austria, accepted for publication in: Risk, Reliability, Uncertainty and Robustness of Water Resources Systems, Cambridge University Press, Cambridge, UK.
  15. K. Hägglöf, The implementation of the stochastic branch and bound method for applications in river basin water quality management, Working paper WP-96-89, International Institute for Applied Systems Analysis, Laxenburg, Austria, 1996.
    Google Scholar
  16. R. Horst, H. Tuy, Global Optimization, Springer, Berlin, 1990.
    MATH Google Scholar

Download references