Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems
Journal of Optimization Theory and Applications
Universal alignment probabilities and subset selection for ordinal optimization
Journal of Optimization Theory and Applications
An explanation of ordinal optimization: soft computing for hard problems
Information Sciences: an International Journal
Stochastic Comparison Algorithm for Discrete Optimization with Estimation
SIAM Journal on Optimization
Constraint ordinal optimization
Information Sciences—Applications: An International Journal
Application of multi-objective simulation-optimization techniques to inventory management problems
WSC '05 Proceedings of the 37th conference on Winter simulation
Brief An ordinal optimization approach to optimal control problems
Automatica (Journal of IFAC)
Hi-index | 22.14 |
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.