Efficient algorithm for virtual topology design in multihop lightwave networks
IEEE/ACM Transactions on Networking (TON)
An ordinal optimization approach to a token partition problem for stochastic timed event graphs
WSC '94 Proceedings of the 26th conference on Winter simulation
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
Universal alignment probability revisited
Journal of Optimization Theory and Applications
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Constrained Ordinal Optimization--A Feasibility Model Based Approach
Discrete Event Dynamic Systems
| Hi-index | 0.98 |
The evaluation of performance of a design for complex discrete event systems through simulation is usually very time consuming. Optimizing the system performance becomes even more computationally infeasible. Ordinal optimization (OO) is a technique introduced to attack this difficulty in system design by looking at ''order'' in performances among designs instead of ''value'' and providing a probability guarantee for a good enough solution instead of the best for sure. The selection rule, known as the rule to decide which subset of designs to select as the OO solution, is a key step in applying the OO method. Pairwise elimination and round robin comparison are two selection rule examples. Many other selection rules are also frequently used in the ordinal optimization literature. To compare selection rules, we first identify some general facts about selection rules. Then we use regression functions to quantify the efficiency of a group of selection rules, including some frequently used rules. A procedure to predict good selection rules is proposed and verified by simulation and by examples. Selection rules that work well most of the time are recommended.