Supplementary code to the paper: Flexible Enterprise Optimization With Constraint Programming

doi: 10.4121/17060642.v1
The doi 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: 10.4121/17060642
Datacite citation style:
Sytze Andringa; Yorke-Smith, Neil (2021): Supplementary code to the paper: Flexible Enterprise Optimization With Constraint Programming. Version 1. 4TU.ResearchData. software.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
This repository contains experimental data for the experiments performed in "Flexible Enterprise Optimization With Constraint Programming". The experiments are based on how enterprise models can be "solved" through CP. Due to page limit reasons, not all experiments were discussed in the paper.

The experiments in this repository can be divided into three categories.

1. Petri-nets. Here, petri net models based on enterprise models are solved through MiniZinc.
2. Netlogo simulation + Neural network. Here, neural networks are trained on NetLogo simulation models. Then, these neural networks are embedded into MiniZinc, and used to find solutions to it in a multi objective sense.
3. Other experiments. Here, a simple supply chain of a pizza restaurant, as well as a hospital case (FHCC) that was based on a DEMO model, are formulated as a CP model. These experiments are not dicussed in the thesis.
  • 2021-12-15 first online, published, posted
Python, Minizinc
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology.


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