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ABSTRACT

We propose fully sequential indifference-zone selection procedures that are specifically for use within an optimization-via-simulation algorithm when simulation is costly and partial or complete information on solutions previously visited is maintained. Sequential Selection with Memory guarantees to select the best or near-best alternative with a user-specified probability when some solutions have already been sampled and their previous samples are retained. For the case when only summary information is retained, we derive a modified procedure. We illustrate how our procedure can be applied to optimization-via-simulation problems and compare its performance with other methods by numerical examples.

REFERENCES

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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Probabilistic algorithms (including Monte Carlo)

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Stochastic processes

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.8 Types of Simulation
          Subjects: Monte Carlo

General Terms:
Algorithms, Performance, Theory