Skip to main content
Log in

A piecewise linear upper bound on the network recourse function

  • Published:
Mathematical Programming Submit manuscript

Abstract

We consider the optimal value of a pure minimum cost network flow problem as a function of supply, demand and arc capacities. We present a new piecewise linear upper bound on this function, which is called the network recourse function. The bound is compared to the standard Madansky bound, and is shown computationally to be a little weaker, but much faster to find. The amount of work is linear in the number of stochastic variables, not exponential as is the case for the Madansky bound. Therefore, the reduction in work increases as the number of stochastic variables increases. Computational results are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. S.W. Wallace, “Investing in arcs in a network to maximize the expected max flow,” Report no. 852330-2, Chr. Michelsen Institute, Bergen, Norway (1985),Networks, to appear.

    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.R. Birge and S.W. Wallace, “Refining bounds for stochastic linear programs with linearly transformed random variables,”Operations Research Letters 5 (1986) 73–77.

    Article  MATH  MathSciNet  Google Scholar 

  4. K. Frauendorfer, “Solving SLP recourse problems with arbitrary multivariate distributions—the dependent case, Manuscript from Institute für Operations Research der Universität Zürich (1986).

  5. R. van Slyke and R.J-B. Wets, “L-shaped linear programs with applications to optimal control and stochastic programming,”SIAM Journal on Applied Mathematics 17 (1969) 638–663.

    Article  MATH  MathSciNet  Google Scholar 

  6. J.F. Benders, “Partitioning procedures for solving mixed-variables programming problems,”Numerische Mathematik 4 (1962) 238–252.

    Article  MATH  MathSciNet  Google Scholar 

  7. S.W. Wallace, “A two-stage stochastic facility-location problem with time-dependent supply” in: Y. Ermoliev and R.J.-B. Wets (eds.),Numerical methods in stochastic programming (Springer Verlag, Berlin), to appear.

  8. H. Frank and I.T. Brisch,Communication, transmission and transporation networks (Addison-Wesley, Reading, MA, 1971).

    Google Scholar 

  9. T.L. Magnanti and R.T. Wong, “Network design and transporation planning: models and algorithms,”Transporation Science 18 (1984) 1–55.

    Google Scholar 

  10. J.R. Birge and R.J.-B. Wets, “Designing approximation schemes for stochastic optimization problems, in particular for stochastic programs with recourse,”Mathematical Programming Study 27 (1986) 54–102.

    MATH  MathSciNet  Google Scholar 

  11. A. Madansky, “Bounds on the expectation of a convex function of a multivariate random variable,”Annals of Mathematical Statistics 30 (1959) 743–746.

    MathSciNet  MATH  Google Scholar 

  12. P. Kall and D. Stoyan, “Solving stochastic programming problems with recourse including error bounds,”Optimization 13 (1982) 431–447.

    MATH  MathSciNet  Google Scholar 

  13. J.R. Birge and R.J.-B. Wets, “Generating upper bounds on the expected value of a convex function with applications to stochastic programming,” Department of Industrial and Operations Engineering, The University of Michigan, Technical Report 85-14 (1985).

  14. H. Gassmann and W.T. Ziemba, “A tight upper bound for the expectation of a convex function of a multivariate random variable.”Mathematical Programming Study 27 (1986) 39–53.

    MATH  MathSciNet  Google Scholar 

  15. R.T. Rockafellar,Network flows and monotropic optimization (John Wiley & Sons, New York, 1984).

    MATH  Google Scholar 

  16. D. Klingman, A. Napier and J. Stutz, “NETGEN—A program for generating large scale (un)capacitated assignment, transportation and minimum cost flow network problems,”Management Science 20 (1974) 814–822.

    Article  MathSciNet  MATH  Google Scholar 

  17. P.A. Jensen and J.W. Barnes,Network flow programming (John Wiley & Sons, New York, 1980).

    MATH  Google Scholar 

  18. S.W. Wallace, “Solving stochastic programs with network recourse,”Networks 16 (1986) 295–317.

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wallace, S.W. A piecewise linear upper bound on the network recourse function. Mathematical Programming 38, 133–146 (1987). https://doi.org/10.1007/BF02604638

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02604638

Key words

Navigation