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Stochastic Integer Programming By Dynamic Programming

Author

Listed:
  • B.J. Lageweg
  • J.K Lenstra
  • A.H.G. RinnooyKan
  • L. Stougie
  • A.H.G. Rinnooy Kan

Abstract

Stochastic integer programming is a suitable tool for modeling hierarchical decision situations with combinatorial features. In continuation of our work on the design and analysis of heuristics for such problems, we now try to find optimal solutions. Dynamic programming techniques can be used to exploit the structure of two–stage scheduling, bin packing and multiknapsack problems. Numerical results for small instances of these problems are presented.

Suggested Citation

  • B.J. Lageweg & J.K Lenstra & A.H.G. RinnooyKan & L. Stougie & A.H.G. Rinnooy Kan, 1985. "Stochastic Integer Programming By Dynamic Programming," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 39(2), pages 97-113, June.
    • Handle: RePEc:bla:stanee:v:39:y:1985:i:2:p:97-113
    DOI: 10.1111/j.1467-9574.1985.tb01131.x
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    Cited by:

    1. Samer Takriti & John R. Birge, 2000. "Lagrangian Solution Techniques and Bounds for Loosely Coupled Mixed-Integer Stochastic Programs," Operations Research, INFORMS, vol. 48(1), pages 91-98, February.

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