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.
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