TY - GEN
T1 - Calibrating Simulation Models with Sparse Data
T2 - 2022 Winter Simulation Conference, WSC 2022
AU - Van Schilt, Isabelle M.
AU - Kwakkel, Jan H.
AU - Mense, Jelte P.
AU - Verbraeck, Alexander
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2022
Y1 - 2022
N2 - COVID-19 related crimes like counterfeit Personal Protective Equipment (PPE) involve complex supply chains with partly unobservable behavior and sparse data, making it challenging to construct a reliable simulation model. Model calibration can help with this, as it is the process of tuning and estimating the model parameters with observed data of the system. A subset of model calibration techniques seems to be able to deal with sparse data in other fields: Genetic Algorithms and Bayesian Inference. However, it is unknown how these techniques perform when accurately calibrating simulation models with sparse data. This research analyzes the quality-of-fit of these two model calibration techniques for a counterfeit PPE simulation model given an increasing degree of data sparseness. The results demonstrate that these techniques are suitable for calibrating a linear supply chain model with randomly missing values. Further research should focus on other techniques, larger set of models, and structural uncertainty.
AB - COVID-19 related crimes like counterfeit Personal Protective Equipment (PPE) involve complex supply chains with partly unobservable behavior and sparse data, making it challenging to construct a reliable simulation model. Model calibration can help with this, as it is the process of tuning and estimating the model parameters with observed data of the system. A subset of model calibration techniques seems to be able to deal with sparse data in other fields: Genetic Algorithms and Bayesian Inference. However, it is unknown how these techniques perform when accurately calibrating simulation models with sparse data. This research analyzes the quality-of-fit of these two model calibration techniques for a counterfeit PPE simulation model given an increasing degree of data sparseness. The results demonstrate that these techniques are suitable for calibrating a linear supply chain model with randomly missing values. Further research should focus on other techniques, larger set of models, and structural uncertainty.
UR - http://www.scopus.com/inward/record.url?scp=85147450118&partnerID=8YFLogxK
U2 - 10.1109/WSC57314.2022.10015241
DO - 10.1109/WSC57314.2022.10015241
M3 - Conference contribution
AN - SCOPUS:85147450118
T3 - Proceedings - Winter Simulation Conference
SP - 496
EP - 507
BT - Proceedings of the 2022 Winter Simulation Conference, WSC 2022
A2 - Feng, B.
A2 - Pedrielli, G.
A2 - Peng, Y.
A2 - Shashaani, S.
A2 - Song, E.
A2 - Corlu, C.G.
A2 - Lee, L.H.
A2 - Chew, E.P.
A2 - Roeder, T.
A2 - Lendermann, P.
PB - Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 11 December 2022 through 14 December 2022
ER -
