Code: Parametric Calibration for Supply Chain Simulation Models with Sparse Data

This code is part of the Ph.D. thesis of Isabelle M. van Schilt, Delft University of Technology.<br>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 <code>pydream</code>, Genetic Algorithms using <code>Platypus</code>, and Powell's Method using <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.<br>This code is an extension of the <code>celibration</code> library, making it easy to plugin different calibration models, distance metrics and functions, and data.<br>Note that this code uses an old version of pydsol, which is included in the zip file.<br>