A new framework for parallel ranking & selection using an adaptive standard. Pei, L., Nelson, B. L., & Hunter, S. R. 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

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.

@inproceedings{2018peinelhunWSC, Year = {2018}, Author = {L. Pei and B. L. Nelson and S. R. Hunter}, Title = {A new framework for parallel ranking \& selection using an adaptive standard}, Booktitle = {Proceedings of the 2018 Winter Simulation Conference}, Editor = {M. Rabe and A. A. Juan and N. Mustafee and A. Skoogh and S. Jain and B. Johansson}, Publisher = {IEEE}, Address = {Piscataway, NJ}, doi = {10.1109/WSC.2018.8632457}, pages = {2201--2212}, url = {http://web.ics.purdue.edu/~hunter63/PAPERS/2018peinelhunWSC.pdf}, abstract = {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.}, keywords = {simulation optimization > single-objective > ranking and selection > parallel}}

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