Stochastic resource allocation using a predictor-based heuristic for optimization via simulation

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.