# Online Fair Allocation of Perishable Resources

arXiv: https://arxiv.org/abs/2406.02402 · HTML: https://mlsanity.com/paper/2406.02402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Individual Fairness in Hindsight](https://mlsanity.com/paper/1812.04069/) (2018)
- [Fair Algorithms for Learning in Allocation Problems](https://mlsanity.com/paper/1808.10549/) (2018)
- [Regularized Online Allocation Problems: Fairness and Beyond](https://mlsanity.com/paper/2007.00514/) (2020)
- [Online Fair Division: analysing a Food Bank problem](https://mlsanity.com/paper/1502.07571/) (2015)
- [Fair Dynamic Rationing](https://mlsanity.com/paper/2102.01240/) (2021)
- [Fairness and Utilization in Allocating Resources with Uncertain Demand](https://mlsanity.com/paper/1906.09050/) (2019)
- [Online Fair Division: A Survey](https://mlsanity.com/paper/1911.09488/) (2019)
- [Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve](https://mlsanity.com/paper/2105.05308/) (2021)
- [Nash Social Welfare Approximation for Strategic Agents](https://mlsanity.com/paper/1607.01569/) (2016)
- [Fairness-Efficiency Tradeoffs in Dynamic Fair Division](https://mlsanity.com/paper/1907.11672/) (2019)
- [Online Vector Balancing and Geometric Discrepancy](https://mlsanity.com/paper/1912.03350/) (2019)
- [Fair Assortment Planning](https://mlsanity.com/paper/2208.07341/) (2022)
- [Predict and Match: Prophet Inequalities with Uncertain Supply](https://mlsanity.com/paper/2001.06779/) (2020)
- [Monotone and Online Fair Division](https://mlsanity.com/paper/2006.15891/) (2020)
- [Sequential Fair Resource Allocation under a Markov Decision Process Framework](https://mlsanity.com/paper/2301.03758/) (2023)
- [Fair Incentives for Repeated Engagement](https://mlsanity.com/paper/2111.00002/) (2021)

## Built on by (most cited first, 1 of 1)

- [Online Fair Division for Personalized 2-Value Instances](https://mlsanity.com/paper/2505.22174/) (2025)

## Abstract (arXiv)

We consider a practically motivated variant of the canonical online fair allocation problem: a decision-maker has a budget of perishable resources to allocate over a fixed number of rounds. Each round sees a random number of arrivals, and the decision-maker must commit to an allocation for these individuals before moving on to the next round. The goal is to construct a sequence of allocations that is envy-free and efficient. Our work makes two important contributions toward this problem: we first derive strong lower bounds on the optimal envy-efficiency trade-off, demonstrating that a decision-maker is fundamentally limited in what she can hope to achieve relative to the no-perishing setting; we then design an algorithm achieving these lower bounds which takes as input (i) a prediction of the perishing order, and (ii) a desired bound on envy. Given the remaining budget in each period, the algorithm uses forecasts of future demand perishing to adaptively choose from one of two carefully constructed guardrail quantities. We demonstrate our algorithm's strong numerical performance, and state-of-the-art, perishing-agnostic algorithms' inefficacy, on simulations calibrated to a real-world dataset.