Optimal computing budget allocation for Monte Carlo simulation with application to product design

Chun Hung Chen, Karen Donohue, Enver Yücesan, Jianwu Lin

Research output: Contribution to journalConference articlepeer-review

43 Scopus citations


Ordinal optimization has emerged as an efficient technique for simulation and optimization, converging exponentially in many cases. In this paper, we present a new computing budget allocation approach that further enhances the efficiency of ordinal optimization. Our approach intelligently determines the best allocation of simulation trials or samples necessary to maximize the probability of identifying the optimal ordinal solution. We illustrate the approach's benefits and ease of use by applying it to two electronic circuit design problems. Numerical results indicate the approach yields significant savings in computation time above and beyond the use of ordinal optimization.

Original languageEnglish (US)
Pages (from-to)57-74
Number of pages18
JournalSimulation Modelling Practice and Theory
Issue number1
StatePublished - Mar 15 2003
EventModelling and Simulation: Analysis, Design and Optimisation of Industrial Systems - Troyes, France
Duration: Apr 25 2001Apr 27 2001

Bibliographical note

Funding Information:
This work has been supported in part by NSF under grants DMI-9732173, DMI-0002900, DMI-0049062, by Sandia National Laboratories under contract BD-0618, and by George Mason University Research Foundation.

Copyright 2008 Elsevier B.V., All rights reserved.


  • Computing budget allocation
  • Intelligent simulation
  • Manufacturing design
  • Monte Carlo simulation
  • Yield analysis


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