Alexander Hess
419c7e6bf6
- so far, the check constraints were untested
+ the tests mirror the tests for the `ReplayedOrder` model
with some variations
- shorten some import names
- fix some typos
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| .github/workflows | ||
| docs | ||
| migrations | ||
| research | ||
| src/urban_meal_delivery | ||
| tests | ||
| .gitignore | ||
| .gitmodules | ||
| .pre-commit-config.yaml | ||
| alembic.ini | ||
| LICENSE.txt | ||
| noxfile.py | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
| setup.cfg | ||
Urban Meal Delivery
This repository holds code analyzing the data of an undisclosed urban meal delivery platform (UDP) operating in France from January 2016 to January 2017. The goal is to optimize the platform's delivery process involving independent couriers.
Structure
The analysis is structured into the following stages that iteratively build on each other.
Data Cleaning
The UDP provided its raw data as a PostgreSQL dump.
This notebook
cleans the data extensively
and maps them onto the ORM models
defined in the urban-meal-delivery package
that is developed in the src/ folder
and contains all source code to drive the analyses.
Due to a non-disclosure agreement with the UDP, neither the raw nor the cleaned data are published as of now. However, previews of the data can be seen throughout the research/ folder.
Tactical Demand Forecasting
Before any optimizations of the UDP's operations are done, a demand forecasting system for tactical purposes is implemented. To achieve that, the cities first undergo a gridification step where each pickup location is assigned into a pixel on a "checker board"-like grid. The main part of the source code that implements that is in this file. Visualizations of the various grids can be found in the visualizations/ folder and in this notebook.
Then, demand is aggregated on a per-pixel level
and different kinds of order time series are generated.
The latter are the input to different kinds of forecasting *Models.
They all have in common that they predict demand into the short-term future (e.g., one hour)
and are thus used for tactical purposes, in particular predictive routing (cf., next section).
The details of how this works can be found in the first academic paper
published in the context of this research project
and titled "Real-time Demand Forecasting for an Urban Delivery Platform"
(cf., the repository with the LaTeX files).
All demand forecasting related code is in the forecasts/ sub-package.
Predictive Routing
Shift & Capacity Planning
Installation & Contribution
To play with the code developed for the analyses,
you can clone the project with git
and install the contained urban-meal-delivery package
and all its dependencies
in a virtual environment
with poetry:
git clone https://github.com/webartifex/urban-meal-delivery.git
and
poetry install --extras research
The --extras option is necessary as the non-develop dependencies
are structured in the pyproject.toml file
into dependencies related to only the urban-meal-delivery source code package
and dependencies used to run the Jupyter environment
with the analyses.
Contributions are welcome. Use the issues tab. The project is licensed under the MIT license.