Optimal Meal Schedule for a Local Nonprofit Using LLM-Aided Data Extraction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914168426201088 |
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| author | Marin, Sergio Nguyen, Nhu Max Zheng Weaver, Christina M. |
| author_facet | Marin, Sergio Nguyen, Nhu Max Zheng Weaver, Christina M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Meal Schedule for a Local Nonprofit Using LLM-Aided Data Extraction Marin, Sergio Nguyen, Nhu Max Zheng Weaver, Christina M. Computers and Society Optimization and Control Applications 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. |
| title | Optimal Meal Schedule for a Local Nonprofit Using LLM-Aided Data Extraction |
| topic | Computers and Society Optimization and Control Applications |
| url | https://arxiv.org/abs/2511.18483 |