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A package for generating, ranking, analysing and visualising the best candidate solutions for discrete facility location problems

Project description

lokigi - Generate, rank, analyse and visualise the best candidate solutions for facility location problems

Project info: Code licence ORCID All Contributors
Installation: PyPI
Metrics: PyPI downloads all time PyPI downloads monthly PyPI downloads weekly GitHub Repo stars
Activity: GitHub forks GitHub last commit GitHub Release Date GitHub open-pull-requests
Build & quality status: Project Status: Active – The project has reached a stable, usable state and is being actively developed. Tests Documentation
Supported platforms: 3.11|3.12|3.13|3.14 OS

lokigi means 'to locate' or 'to place' in the Esperanto language.

(or it's the backronym 'Location Optimisation: K-best solution Inspection, Generation & Insights' - whichever floats your boat)

lokigi exists to make the process of providing decision support for healthcare problems with a geographical component easier.

A range of fantastic libraries exist for geographic optimization (e.g. spopt), but many use linear programming to optimize the solution, meaning you emerge with a single optimal solution. For healthcare contexts, this often isn't ideal - decision makers need a range of near-optimal solutions to balance against real-world constraints.

Building on work from Dr Tom Monks and previous rounds of the Health Service Modelling Associates programme, lokigi is designed to make it easier to beginner programmers to tackle location optimization problems for the benefit of their organisations.

Lokigi is planned to expand to boundary optimisation problems in the future.

Getting started

Head to the documentation to find out how to use the package.

Install the package with pip install lokigi

[!WARNING] Lokigi is currently in an alpha state and not advised to be used for production. However, we would welcome feedback as we move towards a full release.

Conda support coming very soon.

Contributors

Sammi Rosser
Sammi Rosser

💻 📖 ⚠️ 🐛 🖋 🎨 💡 🤔 🚇 🚧 📆 🔬
Tom Monks
Tom Monks

💻 🤔 🧑‍🏫
Dr Daniel Chalk
Dr Daniel Chalk

🧑‍🏫
Amy Heather
Amy Heather

🚇

Note

This package builds on work from the metapy project from Dr Tom Monks.

Metapy can be found here.

Metapy is release under the MIT licence. The licence is reproduced below in line with the terms of the licence.

MIT License

Copyright (c) 2020 health-data-science-OR

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Modified metapy code is noted within the source code.

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