Online Fair Allocation of Perishable Resources
研究食品银行、血库等场景中易逝资源如何在需求不确定且库存可能过期时公平高效分配，提出算法匹配公平与效率的帕累托前沿，模拟显示对现实数据有效。
Fair Allocation with Perishable Resources Food banks, blood banks, and vaccine programs routinely face a hard operational question: How can scarce supplies be distributed fairly and efficiently when tomorrow’s need is uncertain and today’s inventory may expire? In “Online Fair Allocation of Perishable Resources,” Sid Banerjee, Chamsi Hssaine, and Sean R. Sinclair study this problem in a setting where a decision maker must allocate a limited budget of perishable resources over time to stochastically arriving demand. The goal is to construct allocations that balance two competing objectives: fairness, where individuals receive similar allocations over time, and efficiency, where the available supply of goods is not wasted. The paper shows that perishability fundamentally alters the achievable fairness-efficiency Pareto frontier, relative to settings where resources do not expire. The authors first derive lower bounds characterizing the unavoidable perishing-induced loss incurred by any algorithm; this loss drives the modified frontier. They then design an algorithm that provably matches this Pareto frontier, up to polylogarithmic factors. The algorithm uses forecasts of future demand and perishing to adaptively choose between two guardrail quantities that control allocation decisions. Simulations calibrated to real-world data show strong performance and demonstrate that perishing-agnostic methods can perform poorly.