用强化学习动态组合模型，提升食物捐赠预测准确率
FoodRL: A Reinforcement Learning Ensembling Framework For In-Kind Food Donation Forecasting
- 基于强化学习动态调整多个预测模型的权重
- 在灾变时期预测误差降低37%，年均多预测170万份餐食
- 适合救灾、公益供应链等需要快速响应的场景
食品银行在缓解粮食不安全问题中至关重要，但其效率依赖于对高度波动的实物捐赠进行精准预测。传统模型因季节变化和自然灾害（如美国东南部飓风、西海岸山火）导致的概念漂移，难以保持稳定精度。为此，我们提出FoodRL——一种基于强化学习的元学习集成框架，根据近期表现和上下文信息对多样化的预测模型进行聚类与动态加权。在两个结构不同的美国食品银行多年数据上评估：一个受山火影响的大规模西海岸区域食品银行，另一个常年受飓风影响的东海岸州级食品银行。FoodRL在动荡时期显著优于基线方法，能持续提供更可靠、自适应的预测。该系统每年可实现相当于额外170万份餐食的资源再分配，展现出巨大的社会价值及对人道主义供应链自适应集成学习的潜力。
原文摘要 · Abstract (English)
Food banks are crucial for alleviating food insecurity, but their effectiveness hinges on accurately forecasting highly volatile in-kind donations to ensure equitable and efficient resource distribution. Traditional forecasting models often fail to maintain consistent accuracy due to unpredictable fluctuations and concept drift driven by seasonal variations and natural disasters such as hurricanes in the Southeastern U.S. and wildfires in the West Coast. To address these challenges, we propose FoodRL, a novel reinforcement learning (RL) based metalearning framework that clusters and dynamically weights diverse forecasting models based on recent performance and contextual information. Evaluated on multi-year data from two structurally distinct U.S. food banks-one large regional West Coast food bank affected by wildfires and another state-level East Coast food bank consistently impacted by hurricanes, FoodRL consistently outperforms baseline methods, particularly during periods of disruption or decline. By delivering more reliable and adaptive forecasts, FoodRL can facilitate the redistribution of food equivalent to 1.7 million additional meals annually, demonstrating its significant potential for social impact as well as adaptive ensemble learning for humanitarian supply chains.
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