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
Empirical comparison of search algorithms for discrete event simulation
Computers and Industrial Engineering
Universal alignment probability revisited
Journal of Optimization Theory and Applications
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Simulation-based optimization: practical introduction to simulation optimization
Proceedings of the 35th conference on Winter simulation: driving innovation
On the quasi-Newton training method for feed-forward neural networks
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Gradient-based simulation optimization
Proceedings of the 38th conference on Winter simulation
Ordinal Optimization: Soft Optimization for Hard Problems
Ordinal Optimization: Soft Optimization for Hard Problems
An ordinal optimization theory-based algorithm for large distributed power systems
Computers & Mathematics with Applications
Evolutionary algorithm for stochastic job shop scheduling with random processing time
Expert Systems with Applications: An International Journal
A random search heuristic for a multi-objective production planning
Computers and Industrial Engineering
Ordinal optimization based approach to the optimal resource allocation of grid computing system
Mathematical and Computer Modelling: An International Journal
Optimal base-stock policy of the assemble-to-order systems
Artificial Life and Robotics
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In this paper, we have proposed an ordinal optimization theory-based two-stage algorithm to solve for a good enough solution of the stochastic simulation optimization problem with huge input-variable space @Q. In the first stage, we construct a crude but effective model for the considered problem based on an artificial neural network. This crude model will then be used as a fitness function evaluation tool in a genetic algorithm to select N excellent settings from @Q. In the second stage, starting from the selected N excellent settings we proceed with the existing goal softening searching procedures to search for a good enough solution of the considered problem. We applied the proposed algorithm to the reduction of overkills and retests in a wafer probe testing process, which is formulated as a stochastic simulation optimization problem that consists of a huge input-variable space formed by the vector of threshold values in the testing process. The vector of good enough threshold values obtained by the proposed algorithm is promising in the aspects of solution quality and computational efficiency. We have also justified the performance of the proposed algorithm in a wafer probe testing process based on the ordinal optimization theory.