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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 57-74 |
| Number of pages | 18 |
| Journal | Simulation Modelling Practice and Theory |
| Volume | 11 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 15 2003 |
| Event | Modelling and Simulation: Analysis, Design and Optimisation of Industrial Systems - Troyes, France Duration: Apr 25 2001 → Apr 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:
Copyright 2008 Elsevier B.V., All rights reserved.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Computing budget allocation
- Intelligent simulation
- Manufacturing design
- Monte Carlo simulation
- Yield analysis
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