TY - GEN
T1 - A new framework for parallel ranking & selection using an adaptive standard
AU - Pei, Linda
AU - Nelson, Barry L.
AU - Hunter, Susan
N1 - Funding Information:
Hunter’s research was partially supported by the National Science Foundation under Grant Number CMMI-1554144. Nelson and Pei’s research was partially supported by the National Science Foundation under Grant Number CMMI-1537060.
Funding Information:
Hunter's research was partially supported by the National Science Foundation under Grant Number CMMI-1554144. Nelson and Pei's research was partially supported by the National Science Foundation under Grant Number CMMI-1537060.
Publisher Copyright:
© 2018 IEEE
PY - 2019/1/31
Y1 - 2019/1/31
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85062640810&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062640810&partnerID=8YFLogxK
U2 - 10.1109/WSC.2018.8632457
DO - 10.1109/WSC.2018.8632457
M3 - Conference contribution
AN - SCOPUS:85062640810
T3 - Proceedings - Winter Simulation Conference
SP - 2201
EP - 2212
BT - WSC 2018 - 2018 Winter Simulation Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Winter Simulation Conference, WSC 2018
Y2 - 9 December 2018 through 12 December 2018
ER -