Data underlying the publication: Simulation-optimization configurations for real-time decision-making in fugitive interception

doi:10.4121/5f3a6a70-377b-42eb-9f46-5fd1141bed78.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: 10.4121/5f3a6a70-377b-42eb-9f46-5fd1141bed78
Datacite citation style:
van Droffelaar, Irene; Mense, Jelte; Kwakkel, Jan; Verbraeck, Alexander (2024): Data underlying the publication: Simulation-optimization configurations for real-time decision-making in fugitive interception. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/5f3a6a70-377b-42eb-9f46-5fd1141bed78.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.


This repository accompanies the paper "Simulation-optimization configurations for real-time decision-making in fugitive interception" (https://doi.org/10.1016/j.simpat.2024.102923).


The optimization algorithm can be found under platypus-fork.zip.


In simopt-configs.zip, the folders _cleaned data_ and _models_ contain all results and software for:

- Sequential Simulation Optimization (abbreviated as _seq_ in file names), solved with MIP solver CBC (https://github.com/coin-or/Cbc)

- Sequential Simulation Optimization (_seq_), solved with metaheuristic Borg

- Simulation Model Optimization (abbreviated as _smo_ in file names), solved with metaheuristic Borg

history
  • 2024-11-25 first online, published, posted
publisher
4TU.ResearchData
format
.py, .ipynb, .png, .csv
funding
  • National Police Artificial Intelligence Lab National Police Artificial Intelligence Lab
organizations
TU Delft, Faculty of Technology, Policy and Management, Department of Multi-Actor Systems (MAS)

DATA

files (2)