用机器学习优化食品银行选址，让贫困家庭更方便拿到食物。
Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach
- 两级优化框架结合聚类与真实道路距离，智能规划网点位置。
- 加州和印第安纳州测试显示，新选址可减少居民平均出行距离。
- 支持收入等社会因素加权，适合政策制定者参考使用。
超过4400万美国人面临食物不安全问题，其中1300万为儿童。全美数千家食品银行和发放点是保障弱势家庭的重要资源。本文提出一种两级优化框架，结合K-Medoids聚类算法与开源路线引擎（OSRM），基于真实道路距离优化食品银行和发放点的选址。该框架还可通过伪加权K-Medoids纳入中位家庭收入等社会因素。在加州和印第安纳州的家庭数据上测试表明，新方案生成的发放点位置优于现有实际分布，显著降低居民出行距离，仅对一级食品银行到发放点的距离略有增加。总体而言，二级效益远超潜在代价，带来净收益。
原文摘要 · Abstract (English)
Over 44 million Americans currently suffer from food insecurity, of whom 13 million are children. Across the United States, thousands of food banks and pantries serve as vital sources of food and other forms of aid for food insecure families. By optimizing food bank and pantry locations, food would become more accessible to families who desperately require it. In this work, we introduce a novel two-level optimization framework, which utilizes the K-Medoids clustering algorithm in conjunction with the Open-Source Routing Machine engine, to optimize food bank and pantry locations based on real road distances to houses and house blocks. Our proposed framework also has the adaptability to factor in considerations such as median household income using a pseudo-weighted K-Medoids algorithm. Testing conducted with California and Indiana household data, as well as comparisons with real food bank and pantry locations showed that interestingly, our proposed framework yields food pantry locations superior to those of real existing ones and saves significant distance for households, while there is a marginal penalty on the first level food bank to food pantry distance. Overall, we believe that the second-level benefits of this framework far outweigh any drawbacks and yield a net benefit result.
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