TY - GEN

T1 - Simulation model calibration with correlated knowledge-gradients

AU - Frazier, Peter

AU - Powell, Warren B.

AU - Simão, Hugo P.

N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.

PY - 2009

Y1 - 2009

N2 - We address the problem of calibrating an approximate dynamic programming model, where we need to find a vector of parameters to produce the best fit of the model against historical data. The problem requires adaptively choosing the sequence of parameter settings on which to run the model, where each run of the model requires approximately twelve hours of CPU time and produces noisy non-stationary output. We describe an application of the knowledge-gradient algorithm with correlated beliefs to this problem and show that this algorithm finds a good parameter vector out of a population of one thousand with only three runs of the model.

AB - We address the problem of calibrating an approximate dynamic programming model, where we need to find a vector of parameters to produce the best fit of the model against historical data. The problem requires adaptively choosing the sequence of parameter settings on which to run the model, where each run of the model requires approximately twelve hours of CPU time and produces noisy non-stationary output. We describe an application of the knowledge-gradient algorithm with correlated beliefs to this problem and show that this algorithm finds a good parameter vector out of a population of one thousand with only three runs of the model.

UR - http://www.scopus.com/inward/record.url?scp=77951545355&partnerID=8YFLogxK

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U2 - 10.1109/WSC.2009.5429345

DO - 10.1109/WSC.2009.5429345

M3 - Conference contribution

AN - SCOPUS:77951545355

SN - 9781424457700

T3 - Proceedings - Winter Simulation Conference

SP - 339

EP - 351

BT - Proceedings of the 2009 Winter Simulation Conference, WSC 2009

T2 - 2009 Winter Simulation Conference, WSC 2009

Y2 - 13 December 2009 through 16 December 2009

ER -