Codebase for our work titled "FairAssign: Stochastically Fair Driver Assignment in Gig Delivery Platforms" (FAccT 2023).
These instructions will get you a copy of the project up and running on your local machine.
The algorithms are implemented in Python 3 (Python 3.9)
For solving the linear programs, the following LP Solvers will be needed: Gurobi Optimizer and IBM Cplex
Setup a conda environment using the environment.yml file
conda env create -f environment.yml
conda activate fair_assignData : The data files relevant for assignment are present in ./e-commerce/data/. These files have been obtained here.
Code : Please follow the notebook 'ecomm.ipynb' present in ./e-commerce to reproduce the results for the e-commerce setting.
Data : The food-delivery dataset is confidential. It is available on request.
Code : Please follow the notebook 'food_dlvry.ipynb' present in ./food-delivery to reproduce the results for the food delivery setting.
If you find our work useful, please cite using:
@inproceedings{10.1145/3593013.3594040,
author = {Singh, Daman Deep and Das, Syamantak and Chakraborty, Abhijnan},
title = {FairAssign: Stochastically Fair Driver Assignment in Gig Delivery Platforms},
year = {2023},
isbn = {9798400701924},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3593013.3594040},
doi = {10.1145/3593013.3594040},
booktitle = {Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency},
pages = {753–763},
numpages = {11},
keywords = {Dependent Rounding, Ecommerce Logistics, Fair Driver Assignment, Food Delivery., Last Mile Delivery, Stochastic Fairness},
location = {<conf-loc>, <city>Chicago</city>, <state>IL</state>, <country>USA</country>, </conf-loc>},
series = {FAccT '23}
}