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

DOI:10.4121/2a2a2677-4f73-4bd9-ac0d-e28099c3cc26.v1
The DOI displayed 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: 10.4121/2a2a2677-4f73-4bd9-ac0d-e28099c3cc26
Datacite citation style:
van Schilt, Isabelle (2024): Code: Structural Calibration for Supply Chain Simulation Models with Sparse Data. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/2a2a2677-4f73-4bd9-ac0d-e28099c3cc26.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

This repository is used to calibrate the underlying structure of a stylized supply chain simulation model of counterfeit Personal Protective Equipment (PPE). For this, we use four calibration techniques: Approximate Bayesian Computing using pydream, Bayesian Optimization using bayesian-optimization, 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. We define the structure of a supply chain simulation model as a key value of a dictionary (sorted on betweenness centrality), which is a set of possible supply chain models. The integer is, thus, the decision variable of the calibration.


To use this repository, we need a simulation model developed in pydsol-core and pydsol-model . Additionally, we need a dictionary with various different simulation structures as input, as well as the ground truth data. For this project, we use the repository complex_stylized_supply_chain_model_generator as simulation model.


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


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

History

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

Publisher

4TU.ResearchData

Format

*.py

Organizations

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

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/0da30dbc-541f-44f3-a73d-8fd852b7cb0d.git "structure_calibration_sparse_data"

Or download the latest commit as a ZIP.