Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Sequential Simulation Optimization with Censoring
T2 - 2023 Winter Simulation Conference, WSC 2023
AU - Gibbons, Cedric
AU - Grant, James A.
AU - Szechtman, Roberto
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2024/1/31
Y1 - 2024/1/31
N2 - Sequential Simulation Optimization is an online optimization framework where an operator iterates periodically between collecting data from a real-world system, using stochastic simulation to approximate the optimal values of some operational variables, and setting some choice of variables in the system for the next period. The aim is to converge to an optimum efficiently, as uncertainty due to finite data and finitely many simulations eventually reduces. Using Bike Sharing Systems (BSS) as a motivating example, we analyze a variant where data from the real-world system is subject to censoring, whose nature depends on the system variables selected by the operator. In the BSS setting, censoring is of customer demand, to pick up or drop off bikes. We show that a method built upon Sample Average Approximation attains asymptotically vanishing error in its parameter estimates and specification of the optimal operational variables.
AB - Sequential Simulation Optimization is an online optimization framework where an operator iterates periodically between collecting data from a real-world system, using stochastic simulation to approximate the optimal values of some operational variables, and setting some choice of variables in the system for the next period. The aim is to converge to an optimum efficiently, as uncertainty due to finite data and finitely many simulations eventually reduces. Using Bike Sharing Systems (BSS) as a motivating example, we analyze a variant where data from the real-world system is subject to censoring, whose nature depends on the system variables selected by the operator. In the BSS setting, censoring is of customer demand, to pick up or drop off bikes. We show that a method built upon Sample Average Approximation attains asymptotically vanishing error in its parameter estimates and specification of the optimal operational variables.
U2 - 10.1109/WSC60868.2023.10407246
DO - 10.1109/WSC60868.2023.10407246
M3 - Conference contribution/Paper
AN - SCOPUS:85185380347
T3 - Proceedings - Winter Simulation Conference
SP - 3624
EP - 3635
BT - 2023 Winter Simulation Conference, WSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 December 2023 through 13 December 2023
ER -