Stochastic discrete optimization
SIAM Journal on Control and Optimization
A method for discrete stochastic optimization
Management Science
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Nested Partitions Method for Global Optimization
Operations Research
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Discrete Optimization via Simulation Using COMPASS
Operations Research
Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Stochastic Kriging for Simulation Metamodeling
Operations Research
The effects of common random numbers on stochastic kriging metamodels
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimization via simulation using Gaussian process-based search
Proceedings of the Winter Simulation Conference
An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems
INFORMS Journal on Computing
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We propose to use a global metamodeling technique known as stochastic kriging to improve the efficiency of Discrete Optimization-via-Simulation (DOvS) algorithms. Stochastic kriging metamodel allows the DOvS algorithm to utilize all information collected during the optimization process and identify solutions that are most likely to lead to significant improvement in solution quality. We call the approach Stochastic Kriging for OPtimization Efficiency (SKOPE). In this paper, we integrate SKOPE with a locally convergent DOvS algorithm known as Adaptive Hyperbox Algorithm (AHA). Numerical experiments show that SKOPE significantly improves the performance of AHA in the early stage of optimization, which is very helpful for DOvS applications where the number of simulations for an optimization task is severely limited due to a short decision time window and time-consuming simulation.