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Simulation-Based Optimality Tests for Stochastic Programs

In: Stochastic Programming

Author

Listed:
  • Güzin Bayraksan

    (University of Arizona)

  • David P. Morton

    (The University of Texas at Austin)

  • Amit Partani

    (The University of Texas at Austin)

Abstract

Assessing whether a solution is optimal, or near-optimal, is fundamental in optimization. We describe a simple simulation-based procedure for assessing the quality of a candidate solution to a stochastic program. The procedure is easy to implement, widely applicable, and yields point and interval estimators on the candidate solutions optimality gap. Our simplest procedure allows for significant computational improvements. The improvements we detail aim to reduce computational effort through single- and two-replication procedures, reduce bias via a class of generalized jackknife estimators, and reduce variance by using a randomized quasi-Monte Carlo scheme.

Suggested Citation

  • Güzin Bayraksan & David P. Morton & Amit Partani, 2010. "Simulation-Based Optimality Tests for Stochastic Programs," International Series in Operations Research & Management Science, in: Gerd Infanger (ed.), Stochastic Programming, chapter 0, pages 37-55, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1642-6_3
    DOI: 10.1007/978-1-4419-1642-6_3
    as

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