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
Ant algorithms for discrete optimization
Artificial Life
Universal alignment probability revisited
Journal of Optimization Theory and Applications
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
A New Algorithm for Stochastic Discrete Resource AllocationOptimization
Discrete Event Dynamic Systems
Nested Partitions Method for Global Optimization
Operations Research
Ordinal Optimization: Soft Computing for Hard Problems (International Series on Discrete Event Dynamic Systems)
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ordinal Optimization and Quantification of Heuristic Designs
Discrete Event Dynamic Systems
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Finding the optimal design for a discrete event dynamic system (DEDS) is in general difficult due to the large search space and the simulation-based performance evaluation. Various heuristics have been developed to find good designs. An important question is how to quantify the goodness of the heuristic designs. Inspired by the Ordinal Optimization, which has become an important tool for optimizing DEDS, we provide a method which can quantify the goodness of the design. By comparing with a set of designs that are uniformly sampled, we measure the ordinal performances of heuristic designs, i.e., we quantify the ranks of all (or some of) the heuristic designs among all the designs in the entire search space. The mathematical tool we use is the Hypothesis Testing, and the probability of making Type II error in the quantification is controlled to be under a very low level. The method can be used both when the performances of the designs can be accurately evaluated and when such performances are estimated by a crude but computationally easy model. The method can quantify both heuristics that output a single design and that output a set of designs. The method is demonstrated through numerical examples.