Overview¶
Heuristics, relaxations and experiments for the Constrained Least-cost Tour Problem. Read the docs and try out the open-source code.
Getting started¶
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites¶
Our code base leans heavily on networkx, geopandas and osmnx. The integer programming methods also require you to have access to IBM’s CPLEX optimisation studio (local or cloud).
Installation¶
Install with pip from GitHub:
$ pip3 install git+https://github.com/PatrickOHara/CLT-problem.git
If you need to upgrade:
$ pip3 install --upgrade git+https://github.com/PatrickOHara/CLT-problem.git
Datasets¶
We include two datasets: the pollution dataset and the Crucible dataset.
You can see the raw files under cltproblem/datasets
or use the load_dataset() function to return the processed graph:
import cltproblem as clt
G = clt.util.load_dataset('pollution')
H = clt.util.load_dataset('crucible')
Running the examples¶
Take a look at the examples directory.
Cite our work¶
Please cite our work as:
O’Hara. P., Ramanujan. M. S. & Damoulas. T. (2019). On the Constrained Least-cost Tour Problem. arXiv:1906.07754.
Authors¶
Patrick O’Hara - The Alan Turing Institute
Dr Ramanujan Sridharan - University of Warwick
Dr Theo Damoulas - University of Warwick
License¶
This project is licensed under the MIT License - see the LICENSE.md file for details