The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/a772fd6f-ec0b-4038-8e54-5b9901f060ad

van Schilt, Isabelle (2024): Code: Parametric Calibration for Supply Chain Simulation Models with Sparse Data. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/a772fd6f-ec0b-4038-8e54-5b9901f060ad.v1

Dataset

• Interdisciplinary Engineering
• Engineering
• Transport

calibration, parametric uncertainty, simulation, sparse data, supply chain

CC BY-NC-ND 4.0

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by Isabelle van Schilt

This code is part of the Ph.D. thesis of Isabelle M. van Schilt, Delft University of Technology.

This code is used to calibrate a parameter of a stylized supply chain simulation model of counterfeit Personal Protective Equipment (PPE). For this, we use three calibration techniques: Approximate Bayesian Computing using pydream, Genetic Algorithms using Platypus, and Powell's Method using SciPy. The calibration is done with sparse data, which is generated by degrading the ground truth data on noise, bias, and missing values.

This code is an extension of the celibration library, making it easy to plugin different calibration models, distance metrics and functions, and data.

Note that this code uses an old version of pydsol, which is included in the zip file.

• 2024-07-19 first online, published, posted

4TU.ResearchData

*.py

Calibrating simulation models with sparse data: Counterfeit supply chains during COVID-19

TU Delft, Faculty of Technology, Policy and Management, Department of Multi-Actor Systems (MAS)