A branch and bound method for stochastic global optimization (original) (raw)
Access this article
Subscribe and save
- Starting from 10 chapters or articles per month
- Access and download chapters and articles from more than 300k books and 2,500 journals
- Cancel anytime View plans
Buy Now
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Instant access to the full article PDF.
References
- Yu.M. Ermoliev, Stochastic quasi-gradient methods and their application to systems optimization, Stochastics 4 (1983) 1–37.
MathSciNet Google Scholar - J.R. Birge, Decomposition and partitioning methods for multistage stochastic linear programs, Operations Research 33 (1985) 989–1007.
MATH MathSciNet Google Scholar - 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 - 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 - 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 - V.S. Mikhalevich, A.M. Gupal, V.I. Norkin, Methods of Non-Convex Optimization, Nauka, Moscow, 1987 (in Russian).
MATH Google Scholar - V.I. Norkin, Yu.M. Ermoliev, A. Ruszczyński, On optimal allocation of indivisibles under uncertainty, Operations Research to appear in 1998.
- R. Horst, P.M. Pardalos (Eds.), Handbook of Global Optimization, Kluwer Academic Publishers, Dordrecht, 1994.
Google Scholar - 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 - 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 - A. Prekopa, Logarithmic concave measures and related topics, in: M.A.H. Dempster (Ed.), Stochastic Programming, Academic Press, London, pp. 63–82.
- 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.
- 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.
- 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.
- 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 - R. Horst, H. Tuy, Global Optimization, Springer, Berlin, 1990.
MATH Google Scholar