Track&Know: Genetic p-Median Solver

Quick Start

If you have your own Open Route Service pass it using the ORS_HOST environment variable:

• docker run -p 8000:8000 -e ORS_HOST= https://yourhost/ors ibadkureshi/tnk-pmed:latest

If you are using the public Open Route Service (the API playground) pass you key to the code

• docker run -p 8000:8000 -e ORS_KEY= your key ibadkureshi/tnk-pmed:latest

Note: this is not recommended due to rate limits and the code doesn't optimise against number of api/routing calls

Then open a browser and go to http://localhost:8000/

About Track & Know

• Project Title - Big Data for Mobility Tracking Knowledge Extraction in Urban Areas
• Project Website - https://trackandknowproject.eu/
• Work Package - WP4: Big Data Analytics (BDA Toolbox) [Leader: CNR]
• Task & Deliverable - 4.1 Analytics for mobility patterns detection and forecasting [Leader: UPRC]
• Component Leader - Inlecom Group

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780754.

Background

Location-allocation problems typical deal with provisioning of resources between facilities based on historic demand. The p-median approach is one such model that aims to minimise the total demand-weighted distance between the demand points and the facilities. This NP-Hard problem aims to locate p facilities to serve n demand, by minimising the total demand-weighted distance between the facilities and the demand.

The Track&Know Genetic p-Median Solver uses a genetic algorithm approach to solve the problem in polynomial time. This tool plays an important role in translating mobility information into policy and management recommendations. This project is a parallelised and containerised implementation in Python of a Genetic Algorithm approach to solve the p-Median problem. The underlying model is based on the following research paper:

Alp, O., Erkut, E., & Drezner, Z. (2003). An efficient genetic algorithm for the p-median problem. Annals of Operations research, 122(1-4), 21-42.

Documentation

• Details about the algorithm
• Details about the implementation
• How to contribute
• Other software/tools/models from Track&Know

Maintainers

• Dr. Ibad Kureshi
• Dr. Panos Protopapas
• Ms. Angeliki Mylonaki
• Mr. Tasos Kakouris