In Rabe, M., Juan, A. A., Mustafee, N., Skoogh, A., Jain, S., & Johansson, B., editors, Proceedings of the 2018 Winter Simulation Conference, pages 2201–2212, Piscataway, NJ, 2018. IEEE. Paper doi abstract bibtex
When we have sufficient computational resources to treat a simulation optimization problem as a ranking & selection (R&S) problem, then it can be ``solved.'' R&S is exhaustive search—all feasible solutions are simulated—with meaningful statistical error control. High-performance parallel computing promises to extend the R&S limit to even larger problems, but parallelizing R&S procedures in a way that maintains statistical validity while achieving substantial speed-up is difficult. In this paper we introduce an entirely new framework for R&S called Parallel Adaptive Survivor Selection (PASS) that is specifically engineered to exploit parallel computing environments for solving simulation optimization problems with a very large number of feasible solutions.