TY - GEN

T1 - Stochastic Approximation for simulation Optimization under Input Uncertainty with Streaming Data

AU - Song, Eunhye

AU - Shanbhag, Uday V.

N1 - Publisher Copyright:
© 2019 IEEE.

PY - 2019/12

Y1 - 2019/12

N2 - We consider a simulation optimization problem whose objective function is defined as the expectation of a simulation output based on a continuous decision variable, where the parameters of the simulation input distributions are estimated based on independent and identically distributed streaming data from a real-world system. Finite-sample error in the input parameter estimates causes input uncertainty in the simulation output, which decreases as the data size increases. By viewing the problem through the lens of misspecified stochastic optimization, we develop a stochastic approximation (SA) framework to solve a sequence of problems defined by the sequence of input parameter estimates to increasing levels of exactness. Under suitable assumptions, we observe that the error in the SA solution diminishes to zero in expectation and propose a SA sampling scheme so that the resulting solution iterates converge to the optimal solution under the real-world input distribution at the best possible rate.

AB - We consider a simulation optimization problem whose objective function is defined as the expectation of a simulation output based on a continuous decision variable, where the parameters of the simulation input distributions are estimated based on independent and identically distributed streaming data from a real-world system. Finite-sample error in the input parameter estimates causes input uncertainty in the simulation output, which decreases as the data size increases. By viewing the problem through the lens of misspecified stochastic optimization, we develop a stochastic approximation (SA) framework to solve a sequence of problems defined by the sequence of input parameter estimates to increasing levels of exactness. Under suitable assumptions, we observe that the error in the SA solution diminishes to zero in expectation and propose a SA sampling scheme so that the resulting solution iterates converge to the optimal solution under the real-world input distribution at the best possible rate.

UR - http://www.scopus.com/inward/record.url?scp=85081139587&partnerID=8YFLogxK

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U2 - 10.1109/WSC40007.2019.9004677

DO - 10.1109/WSC40007.2019.9004677

M3 - Conference contribution

AN - SCOPUS:85081139587

T3 - Proceedings - Winter Simulation Conference

SP - 3597

EP - 3608

BT - 2019 Winter Simulation Conference, WSC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 Winter Simulation Conference, WSC 2019

Y2 - 8 December 2019 through 11 December 2019

ER -