Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.automatica.2007.03.011
Title: Multi-objective ordinal optimization for simulation optimization problems
Authors: Teng, S. 
Hay Lee, L. 
Peng Chew, E. 
Keywords: Alignment probability
Multi-objective simulation optimization
Ordinal optimization
Pareto optimality
Issue Date: Nov-2007
Source: Teng, S., Hay Lee, L., Peng Chew, E. (2007-11). Multi-objective ordinal optimization for simulation optimization problems. Automatica 43 (11) : 1884-1895. ScholarBank@NUS Repository. https://doi.org/10.1016/j.automatica.2007.03.011
Abstract: Ordinal optimization (OO) has been successfully applied to accelerate the simulation optimization process with single objective by quickly narrowing down the search space. In this paper, we extend the OO techniques to address multi-objective simulation optimization problems by using the concept of Pareto optimality. We call this technique the multi-objective OO (MOO). To define the good enough set and the selected set, we introduce two performance indices based on the non-dominance relationship among the designs. Then we derive several lower bounds for the alignment probability under various scenarios by using a Bayesian approach. Numerical experiments show that the lower bounds of the alignment probability are valid when they are used to estimate the size of the selected set as well as the expected alignment level. Though the lower bounds are conservative, they have great practical value in terms of narrowing down the search space. © 2007 Elsevier Ltd. All rights reserved.
Source Title: Automatica
URI: http://scholarbank.nus.edu.sg/handle/10635/63194
ISSN: 00051098
DOI: 10.1016/j.automatica.2007.03.011
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