Online Fair Allocation of Perishable Resources
This work addresses fair resource allocation in dynamic, perishable settings, which is incremental as it builds on existing online fair allocation frameworks by incorporating perishability constraints.
The paper tackles the problem of online fair allocation of perishable resources, deriving fundamental lower bounds on the envy-efficiency trade-off and designing an algorithm that achieves these bounds using predictions of perishing order and desired envy bounds, with strong numerical performance demonstrated on real-world simulations.
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.