%0 Generic
%A van Schilt, Isabelle
%D 2024
%T Code: Parametric Calibration for Supply Chain Simulation Models with Sparse Data
%U 
%R 10.4121/a772fd6f-ec0b-4038-8e54-5b9901f060ad.v1
%K simulation
%K calibration
%K sparse data
%K parametric uncertainty
%K supply chain
%X <p>This code is part of the Ph.D. thesis of&nbsp;<a href="https://www.tudelft.nl/staff/i.m.vanschilt/?cHash=74e749835b2a89c6c76b804683ffbbcf" target="_blank">Isabelle M. van Schilt</a>, Delft University of Technology.</p><p></p><p>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&nbsp;<code>pydream</code>, Genetic Algorithms using&nbsp;<code>Platypus</code>, and Powell's Method using&nbsp;<code>SciPy</code>. The calibration is done with sparse data, which is generated by degrading the ground truth data on noise, bias, and missing values.</p><p></p><p>This code is an extension of the&nbsp;<code>celibration</code>&nbsp;library, making it easy to plugin different calibration models, distance metrics and functions, and data.</p><p></p><p>Note that this code uses an old version of pydsol, which is included in the zip file.</p><p></p>
%I 4TU.ResearchData