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.

翻译：我们考虑了经典在线公平分配问题的一个实际驱动的变体：决策者有一批可 perishable 资源，需要在固定轮数内进行分配。每一轮会随机出现一定数量的到达者，决策者必须在本轮内为这些个体确定分配方案，然后才能进入下一轮。目标是构建一系列无嫉妒且高效的分配。我们的工作对此问题做出了两项重要贡献：首先，我们推导了最优嫉妒-效率权衡的强下界，表明与无 perishable 设置相比，决策者所能期望实现的目标存在根本性限制；然后，我们设计了一种达到这些下界的算法，该算法以 (i) perishable 顺序的预测 和 (ii) 期望的嫉妒上界作为输入。给定每一时期的剩余预算，算法利用对未来需求 perishable 的预测，自适应地选择两个精心构造的护栏数量之一。我们通过模拟真实世界数据集校准的结果，展示了我们算法强大的数值性能，以及最先进的、不考虑 perishable 的算法无效性。