cff-version: 1.2.0
abstract: "<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>"
authors:
  - family-names: van Schilt
    given-names: Isabelle
    orcid: "https://orcid.org/0000-0003-3862-1278"
title: "Code: Parametric Calibration for Supply Chain Simulation Models with Sparse Data"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/a772fd6f-ec0b-4038-8e54-5b9901f060ad.v1
license: CC BY-NC-ND 4.0
date-released: 2024-07-19