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An Efficient Simulation Budget Allocation Method Incorporating Regression for Partitioned Domains.

Mark W Brantley1, Loo Hay Lee2, Chun-Hung Chen3

  • 1Goose Point Analysis L.L.C., Covington, LA 70433.

Automatica : the Journal of IFAC, the International Federation of Automatic Control
|June 18, 2014
PubMed
Summary

This study introduces a new simulation method using domain partitioning to improve ranking and selection (R&S) efficiency. The approach enhances the probability of correctly identifying the best design by optimizing sample allocation across partitions.

Keywords:
budget allocationregressionsimulation

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Area of Science:

  • Simulation and Operations Research
  • Statistical Modeling

Background:

  • Simulation-based decision-making is powerful but time-consuming.
  • Existing ranking and selection (R&S) procedures improve simulation efficiency.
  • Regression metamodels enhance efficiency but have limitations like quadratic assumptions and homogeneous noise.

Purpose of the Study:

  • To develop a novel R&S method that overcomes limitations of standard regression metamodels.
  • To improve the efficiency of selecting the best design in complex simulation scenarios.
  • To maximize the probability of correct selection by optimizing sample allocation.

Main Methods:

  • Propose a domain partitioning strategy where each partition approximates a quadratic mean.
  • Develop approximately optimal rules for sample allocation both between and within partitions.
  • Integrate regression metamodels within these partitions to leverage prior information.

Main Results:

  • The proposed method partitions the domain to better fit underlying functions.
  • It provides optimized sample allocation rules for enhanced R&S efficiency.
  • Numerical experiments show significant efficiency gains over existing R&S methods.

Conclusions:

  • The new domain partitioning approach enhances simulation-based R&S efficiency.
  • It effectively addresses limitations of traditional regression metamodels.
  • This method offers a more powerful tool for simulation-aided decision-making.