Optimal computing budget allocation for Monte Carlo simulation with application to product design |
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Affiliation: | 1. Department of Systems Engineering and Operations Research, George Mason University, 4400 University Drive, MS 4A6, Fairfax, VA 22030, USA;2. Department of Operations Management Science, University of Minnesota, Minneapolis, MN 55455, USA;3. Technology Management Area, INSEAD, Fontainebleau 77305, France;4. Department of Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA |
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Abstract: | 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. |
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