Optimal Meal Schedule for a Local Nonprofit Using LLM-Aided Data Extraction

• URL: http://arxiv.org/abs/2511.18483v1
• Date: Sun, 23 Nov 2025 15:05:21 GMT
• Title: Optimal Meal Schedule for a Local Nonprofit Using LLM-Aided Data Extraction
• Authors: Sergio Marin, Nhu Nguyen, Max, Zheng, Christina M. Weaver,
• Abstract summary: We present a data-driven pipeline developed in collaboration with the Power Packs Project, a nonprofit addressing food insecurity in local communities.<n>The system integrates data extraction from PDFs, large language models for ingredient standardization, and binary integer programming to generate a 15-week recipe schedule.<n>All 157 recipes were mapped to a nutritional database and assigned estimated and predicted costs using historical invoice data and category-specific inflation adjustments.
• Score: 3.9271139410453366
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a data-driven pipeline developed in collaboration with the Power Packs Project, a nonprofit addressing food insecurity in local communities. The system integrates data extraction from PDFs, large language models for ingredient standardization, and binary integer programming to generate a 15-week recipe schedule that minimizes projected wholesale costs while meeting nutritional constraints. All 157 recipes were mapped to a nutritional database and assigned estimated and predicted costs using historical invoice data and category-specific inflation adjustments. The model effectively handles real-world price volatility and is structured for easy updates as new recipes or cost data become available. Optimization results show that constraint-based selection yields nutritionally balanced and cost-efficient plans under uncertainty. To facilitate real-time decision-making, we deployed a searchable web platform that integrates analytical models into daily operations by enabling staff to explore recipes by ingredient, category, or through an optimized meal plan.

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