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.

Food insecurity remains a persistent public health emergency in the United States, tightly interwoven with chronic disease, mental illness, and opioid misuse. Yet despite the existence of thousands of food banks and pantries, access remains fragmented: 1) current retrieval systems depend on static directories or generic search engines, which provide incomplete and geographically irrelevant results; 2) LLM-based chatbots offer only vague nutritional suggestions and fail to adapt to real-world constraints such as time, mobility, and transportation; and 3) existing food recommendation systems optimize for culinary diversity but overlook survival-critical needs of food-insecure populations, including immediate proximity, verified availability, and contextual barriers. These limitations risk leaving the most vulnerable individuals, those experiencing homelessness, addiction, or digital illiteracy, unable to access urgently needed resources. To address this, we introduce Food4All, the first multi-agent framework explicitly designed for real-time, context-aware free food retrieval. Food4All unifies three innovations: 1) heterogeneous data aggregation across official databases, community platforms, and social media to provide a continuously updated pool of food resources; 2) a lightweight reinforcement learning algorithm trained on curated cases to optimize for both geographic accessibility and nutritional correctness; and 3) an online feedback loop that dynamically adapts retrieval policies to evolving user needs. By bridging information acquisition, semantic analysis, and decision support, Food4All delivers nutritionally annotated and guidance at the point of need. This framework establishes an urgent step toward scalable, equitable, and intelligent systems that directly support populations facing food insecurity and its compounding health risks.

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.

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.

The article "Headed to Ottawa? Here's what you shouldn't miss!" included recommendations for catching a baseball game, honoring fallen soldiers at a war museum and… swinging by the Ottawa Food Bank. Paris Marx first called out the story on X (formerly Twitter). "People who come to us have jobs and families to support, as well as expenses to pay," the AI-written section about the food bank section read. "Life is already difficult enough. Consider going into it on an empty stomach."

This article was published as a part of the Data Science Blogathon. In today's world, where the population is increasing at an alarming rate, food waste has become a major issue. According to recent statistics, one-third of all food produced globally is wasted. This results in a significant loss of resources and contributes to environmental problems such as greenhouse gas emissions. The food waste problem is not only limited to developed countries but is also prevalent in developing countries. The Food and Agriculture Organization (FAO) estimates that food waste generates about 8% of global greenhouse gas emissions.

Every weekday, The Detail makes sense of the big news stories. This week, we talked about the burgeoning concerns over artificial intelligence, talked to two former political party presidents about their hidden role, visited a food bank operating in the wealthy North Shore, looked at the fight to keep foot-and-mouth disease out of our farms, and finished the week with a new Supreme Court case trying to hold big corporations liable for contributing to climate change. Whakarongo mai to any episodes you might have missed. Artificial intelligence systems running rogue might seem like the stuff of science-fiction, but these systems are increasingly common in many high-tech elements of society, from self-driving cars to digital assistants, facial identification, Netflix recommendations, and much, much more. The capabilities of artificial intelligence are growing at pace; a pace that's outstripping regulatory frameworks.

Companies aren't the only organizations remaking themselves into digital enterprises to meet customers' needs. When the COVID-19 pandemic hit, food banks accelerated their digital trans formations to get more food more quickly to more people in need. When the COVID-19 pandemic hit, food banks accelerated their digital transformations to get more food more quickly to more people in need. One of them is The Greater Boston Food Bank (GBFB), which estimates it distributed 56 percent more food in 2020 than the previous year because of the pandemic. Although crisis-related hardships have begun to recede, demand for charitable food aid in GBFB's eastern Massachusetts service area remains significant.

We waste 1.6 billion tons of food every year while 25 million starve and another billion are malnourished. If one startup in Berlin is successful, just maybe. The global food supply chain is mind-bogglingly complex. Tens of millions of farms feed millions of grocery stores and restaurants, which in turn supply almost eight billion people their daily food. Plus of course there are transport companies, wholesalers, distributors, processors, and delivery companies.

Globally, food waste is a massive problem. According to the Food and Agriculture Organization of the United Nations, around 1.3 billion tonnes of food is wasted globally every year. That's believed to be enough to feed the world's 815 million hungry people, four times over. But thanks to advancements in technology, this problem could one day be eradicated. Grocery technology pioneer Ocado, for example, has been able to slash food wastage rates to just 1 in 6,000 items by using data analytics, machine learning and artificial intelligence to manage its produce.