Bayesian approach to global optimization and application to multiobjective and constrained problems
Journal of Optimization Theory and Applications
A gradient approach for smartly allocating computing budget for discrete event simulation
WSC '96 Proceedings of the 28th conference on Winter simulation
Tabu Search
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
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms
Computers and Operations Research
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
Priority-based assignment and routing of a fleet of unmanned combat aerial vehicles
Computers and Operations Research
Expert Systems with Applications: An International Journal
Implications of heavy tails on simulation-based ordinal optimization
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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
Computers and Operations Research
Consistency of Sequential Bayesian Sampling Policies
SIAM Journal on Control and Optimization
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Some combinatorial stochastic resource allocation problems lack algebraically defined objective functions and hence require optimization via simulation as a mechanism for obtaining good solutions. For this class of problems, we propose a new predictor-based heuristic that uses a distance criterion to perform the solution search. To demonstrate our solution approach, we apply this heuristic to the problem of selecting the proper design configuration of an unmanned aerial system (UAS) fleet so as to maximize mission effectiveness. We compare our approach to black box optimization via simulation approaches (two tabu search-based procedures and a greedy heuristic) and glean both methodological and practical insights.