# Same-Day Delivery with Fairness

arXiv: https://arxiv.org/abs/2007.09541 · HTML: https://mlsanity.com/paper/2007.09541/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance](https://mlsanity.com/paper/2411.18195/) (2024)
- [Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates](https://mlsanity.com/paper/2207.00885/) (2022)
- [To Start Up a Start-Up-Embedding Strategic Demand Development in Operational On-Demand Fulfillment via Reinforcement Learning with Information Shaping](https://mlsanity.com/paper/2504.05633/) (2025)
- [A multi-algorithm approach for operational human resources workload balancing in a last mile urban delivery system](https://mlsanity.com/paper/2601.00023/) (2025)
- [Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap](https://mlsanity.com/paper/2609.24750/) (2026)
- [Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics](https://mlsanity.com/paper/2610.01282/) (2026)

## Abstract (arXiv)

The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic. The fast growth is not without its challenge. In 2016, due to low concentrations of memberships and far distance from the depot, certain minority neighborhoods were excluded from receiving Amazon's SDD service, raising concerns about fairness. In this paper, we study the problem of offering fair SDD-service to customers. The service area is partitioned into different regions. Over the course of a day, customers request for SDD service, and the timing of requests and delivery locations are not known in advance. The dispatcher dynamically assigns vehicles to make deliveries to accepted customers before their delivery deadline. In addition to the overall service rate (utility), we maximize the minimal regional service rate across all regions (fairness). We model the problem as a multi-objective Markov decision process and develop a deep Q-learning solution approach. We introduce a novel transformation of learning from rates to actual services, which creates a stable and efficient learning process. Computational results demonstrate the effectiveness of our approach in alleviating unfairness both spatially and temporally in different customer geographies. We also show this effectiveness is valid with different depot locations, providing businesses with an opportunity to achieve better fairness from any location. Further, we consider the impact of ignoring fairness in service, and results show that our policies eventually outperform the utility-driven baseline when customers have a high expectation on service level.