自动化食品投放：两种选择的力量实现动态且公平的食品分配
Automating Food Drop: The Power of Two Choices for Dynamic and Fair Food Allocation
AI总结：
本文针对Food Drop食品分配中人工匹配耗时且分配不公的问题，提出基于两种随机选择的加权balls-into-bins匹配算法，实现公平与效率平衡的实时自动化分配，并证明了最大负载的高概率上界。
摘要：
食品浪费与食品不安全是两个密切相关且紧迫的全球性问题。世界各地的食品救援组织开展了旨在解决这两个问题的项目。在本文中，我们与印第安纳州的一家非营利组织合作，该组织领导了“Food Drop”项目，该项目旨在将遭拒收的卡车装载食品从垃圾填埋场重新引导至食品银行。目前，卡车装载与食品银行的匹配决策由我们合作组织的一名员工完成。除了这是一项非常耗时的工作外，正如人们对基于人工的匹配决策所预期的那样，分配往往存在偏差：一小部分可能的接收方获得了大部分捐赠。我们此次合作的目标是完全自动化Food Drop。为此，我们需要一种用于实时决策的匹配算法，该算法能够在确保接收食品的食品银行之间的公平性与优化卡车司机的效率之间取得平衡。在本文中，我们描述了在我们为合作组织构建和部署的平台中决定算法选择的理论保证和实验。我们的工作还对负载均衡和balls-into-bins博弈的文献做出了贡献，这可能具有独立的研究意义。具体而言，我们研究了将m个带权重的球分配到n个带权重的箱中的问题，其中每个球有两个非均匀采样的随机箱选择，并证明了在任何箱的最大负载上以高概率成立的上界。
英文摘要：
Food waste and food insecurity are two closely related pressing global issues. Food rescue organizations worldwide run programs aimed at addressing the two problems. In this paper, we partner with a non-profit organization in the state of Indiana that leads \emph{Food Drop}, a program that is designed to redirect rejected truckloads of food away from landfills and into food banks. The truckload to food bank matching decisions are currently made by an employee of our partner organization. In addition to this being a very time-consuming task, as perhaps expected from human-based matching decisions, the allocations are often skewed: a small percentage of the possible recipients receives the majority of donations. Our goal in this partnership is to completely automate Food Drop. In doing so, we need a matching algorithm for making real-time decisions that strikes a balance between ensuring fairness for the food banks that receive the food and optimizing efficiency for the truck drivers. In this paper, we describe the theoretical guarantees and experiments that dictated our choice of algorithm in the platform we built and deployed for our partner organization. Our work also makes contributions to the literature on load balancing and balls-into-bins games, that might be of independent interest. Specifically, we study the allocation of $m$ weighted balls into $n$ weighted bins, where each ball has two non-uniformly sampled random bin choices, and prove upper bounds, that hold with high probability, on the maximum load of any bin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
