Online Fair Allocation of Perishable Resources

TL;DR

This paper addresses online fair allocation of perishable resources, establishing fundamental limits and designing an adaptive algorithm that leverages demand forecasts to optimize envy-freeness and efficiency.

Contribution

It derives lower bounds on envy-efficiency trade-offs in perishable resource allocation and proposes an algorithm that adapts using demand predictions to achieve these bounds.

Findings

The algorithm matches the theoretical lower bounds on envy-efficiency trade-offs.

Numerical simulations show the algorithm outperforms perishing-agnostic methods.

The approach effectively uses demand forecasts to improve allocation fairness and efficiency.

Abstract

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.