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

TL;DR

This paper introduces a data-driven, LLM-assisted system for optimizing meal schedules that balance nutritional needs and costs for a nonprofit addressing food insecurity, with real-time web deployment.

Contribution

It develops an integrated pipeline combining data extraction, language models, and optimization to generate cost-effective, nutritionally balanced meal plans adaptable to real-world price fluctuations.

Findings

Optimized 15-week meal plans meeting nutritional and cost constraints.

System effectively handles price volatility and updates easily.

Deployed a web platform for real-time decision support.

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.