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Optimizing an urban meal delivery platform
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Alexander Hess d5b3efbca1
Add aggregate_orders() function 
- the function queries the database and aggregates the ad-hoc orders
  by pixel and time steps into a demand time series
- implement "heavy" integration tests for `aggregate_orders()`
- make `pandas` a package dependency
- streamline the `Config`
2021-01-07 23:35:13 +01:00
.github/workflows Enable CI with GitHub Actions 2020-08-05 15:38:28 +02:00
docs Add technical documentation for the package 2020-08-05 01:44:29 +02:00
migrations Add Forecast model to ORM layer 2021-01-07 12:59:30 +01:00
research Use globals for the database connection 2021-01-04 20:23:55 +01:00
src/urban_meal_delivery Add aggregate_orders() function 2021-01-07 23:35:13 +01:00
tests Add aggregate_orders() function 2021-01-07 23:35:13 +01:00
.gitignore Fix the "clean-pwd" command in nox 2020-08-11 10:31:54 +02:00
.gitmodules Move submodule with demand-forecasting paper into research folder 2020-12-14 16:21:12 +01:00
.pre-commit-config.yaml Adjust the branch reference fixer's logic 2020-09-30 12:16:00 +02:00
alembic.ini Add database migrations 2020-08-11 10:29:58 +02:00
LICENSE.txt Initial commit 2020-08-03 20:19:42 +02:00
noxfile.py Add Grid.gridify() constructor 2021-01-05 18:58:48 +01:00
poetry.lock Add aggregate_orders() function 2021-01-07 23:35:13 +01:00
pyproject.toml Add aggregate_orders() function 2021-01-07 23:35:13 +01:00
README.md Move notebooks into the research folder 2020-12-14 16:56:27 +01:00
setup.cfg Add aggregate_orders() function 2021-01-07 23:35:13 +01:00

README.md

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.

Real-time Demand Forecasting

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.