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