Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19

Isabelle M. Van Schilt; Jan H. Kwakkel; Jelte P. Mense; Alexander Verbraeck

doi:10.1109/WSC57314.2022.10015241

Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19

Isabelle M. Van Schilt, Jan H. Kwakkel, Jelte P. Mense, Alexander Verbraeck

Policy Analysis

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

87 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 2022 Winter Simulation Conference, WSC 2022
Editors	B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
Publisher	IEEE
Pages	496-507
Number of pages	12
ISBN (Electronic)	9798350309713
DOIs	https://doi.org/10.1109/WSC57314.2022.10015241
Publication status	Published - 2022
Event	2022 Winter Simulation Conference, WSC 2022 - Guilin, China Duration: 11 Dec 2022 → 14 Dec 2022

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2022-December
ISSN (Print)	0891-7736

Conference

Conference	2022 Winter Simulation Conference, WSC 2022
Country/Territory	China
City	Guilin
Period	11/12/22 → 14/12/22

Bibliographical note

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.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Access to Document

10.1109/WSC57314.2022.10015241

Calibrating_Simulation_Models_with_Sparse_Data_Counterfeit_Supply_Chains_During_Covid-19Final published version, 473 KB

Cite this

Van Schilt, I. M., Kwakkel, J. H., Mense, J. P., & Verbraeck, A. (2022). Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19. In B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C. G. Corlu, L. H. Lee, E. P. Chew, T. Roeder, & P. Lendermann (Eds.), Proceedings of the 2022 Winter Simulation Conference, WSC 2022 (pp. 496-507). (Proceedings - Winter Simulation Conference; Vol. 2022-December). IEEE. https://doi.org/10.1109/WSC57314.2022.10015241

Van Schilt, Isabelle M. ; Kwakkel, Jan H. ; Mense, Jelte P. et al. / Calibrating Simulation Models with Sparse Data : Counterfeit Supply Chains During Covid-19. Proceedings of the 2022 Winter Simulation Conference, WSC 2022. editor / B. Feng ; G. Pedrielli ; Y. Peng ; S. Shashaani ; E. Song ; C.G. Corlu ; L.H. Lee ; E.P. Chew ; T. Roeder ; P. Lendermann. IEEE, 2022. pp. 496-507 (Proceedings - Winter Simulation Conference).

@inproceedings{e9259587f1f94798b118def74fb1a15c,

title = "Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19",

abstract = "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. ",

author = "\{Van Schilt\}, \{Isabelle M.\} and Kwakkel, \{Jan H.\} and Mense, \{Jelte P.\} and Alexander Verbraeck",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – 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.; 2022 Winter Simulation Conference, WSC 2022 ; Conference date: 11-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/WSC57314.2022.10015241",

language = "English",

series = "Proceedings - Winter Simulation Conference",

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editor = "B. Feng and G. Pedrielli and Y. Peng and S. Shashaani and E. Song and C.G. Corlu and L.H. Lee and E.P. Chew and T. Roeder and P. Lendermann",

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Van Schilt, IM, Kwakkel, JH, Mense, JP & Verbraeck, A 2022, Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19. in B Feng, G Pedrielli, Y Peng, S Shashaani, E Song, CG Corlu, LH Lee, EP Chew, T Roeder & P Lendermann (eds), Proceedings of the 2022 Winter Simulation Conference, WSC 2022. Proceedings - Winter Simulation Conference, vol. 2022-December, IEEE, pp. 496-507, 2022 Winter Simulation Conference, WSC 2022, Guilin, China, 11/12/22. https://doi.org/10.1109/WSC57314.2022.10015241

Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19. / Van Schilt, Isabelle M.; Kwakkel, Jan H.; Mense, Jelte P. et al.
Proceedings of the 2022 Winter Simulation Conference, WSC 2022. ed. / B. Feng; G. Pedrielli; Y. Peng; S. Shashaani; E. Song; C.G. Corlu; L.H. Lee; E.P. Chew; T. Roeder; P. Lendermann. IEEE, 2022. p. 496-507 (Proceedings - Winter Simulation Conference; Vol. 2022-December).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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.

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A2 - Corlu, C.G.

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A2 - Lendermann, P.

PB - IEEE

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Van Schilt IM, Kwakkel JH, Mense JP, Verbraeck A. Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19. In Feng B, Pedrielli G, Peng Y, Shashaani S, Song E, Corlu CG, Lee LH, Chew EP, Roeder T, Lendermann P, editors, Proceedings of the 2022 Winter Simulation Conference, WSC 2022. IEEE. 2022. p. 496-507. (Proceedings - Winter Simulation Conference). doi: 10.1109/WSC57314.2022.10015241

Calibrating Simulation Models with Sparse Data: Counterfeit Supply Chains During Covid-19

Abstract

Publication series

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Bibliographical note

UN SDGs

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