# Automating Food Drop: The Power of Two Choices for Dynamic and Fair Food Allocation

arXiv: https://arxiv.org/abs/2406.06363 · HTML: https://mlsanity.com/paper/2406.06363/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [Online Fair Division: analysing a Food Bank problem](https://mlsanity.com/paper/1502.07571/) (2015)
- [Universal and Tight Online Algorithms for Generalized-Mean Welfare](https://mlsanity.com/paper/2109.00874/) (2021)
- [Almost Envy-free Repeated Matching in Two-sided Markets](https://mlsanity.com/paper/2009.09336/) (2020)
- [Fair and Efficient Online Allocations with Normalized Valuations](https://mlsanity.com/paper/2009.12405/) (2020)
- [Fair Division with Allocator's Preference](https://mlsanity.com/paper/2310.03475/) (2023)
- [Two-Sided Matching Meets Fair Division](https://mlsanity.com/paper/2107.07404/) (2021)
- [Fair Division with Two-Sided Preferences](https://mlsanity.com/paper/2206.05879/) (2022)
- [Balanced Allocations: A Simple Proof for the Heavily Loaded Case](https://mlsanity.com/paper/1310.5367/) (2013)

## Built on by (most cited first, 3 of 3)

- [Honor Among Bandits: No-Regret Learning for Online Fair Division](https://mlsanity.com/paper/2407.01795/) (2024)
- [Improved Regret Bounds for Online Fair Division with Bandit Learning](https://mlsanity.com/paper/2501.07022/) (2025)
- [RescueLens: LLM-Powered Triage and Action on Volunteer Feedback for Food Rescue](https://mlsanity.com/paper/2511.15698/) (2025)

## Abstract (arXiv)

Food waste and food insecurity are two closely related pressing global issues. Food rescue organizations worldwide run programs aimed at addressing the two problems. In this paper, we partner with a non-profit organization in the state of Indiana that leads Food Drop, a program that is designed to redirect rejected truckloads of food away from landfills and into food banks. The truckload to food bank matching decisions are currently made by an employee of our partner organization. In addition to this being a very time-consuming task, as perhaps expected from human-based matching decisions, the allocations are often skewed: a small percentage of the possible recipients receives the majority of donations. Our goal in this partnership is to completely automate Food Drop. In doing so, we need a matching algorithm for making real-time decisions that strikes a balance between ensuring fairness for the food banks that receive the food and optimizing efficiency for the truck drivers. In this paper, we describe the theoretical guarantees and experiments that dictated our choice of algorithm in the platform we built and deployed for our partner organization. Our work also makes contributions to the literature on load balancing and balls-into-bins games, that might be of independent interest. Specifically, we study the allocation of m weighted balls into n weighted bins, where each ball has two non-uniformly sampled random bin choices, and prove upper bounds, that hold with high probability, on the maximum load of any bin.