Automated School Lunch Menu Planning with Machine Learning Algorithms

This study introduces an automated menu planning system for schools that leverages machine learning to optimize and streamline menu creation, focusing on nutritional guidelines, cost-effectiveness, and variety. The system comprises two main algorithms: the menu combination algorithm, which employs content-based filtering to select diverse recipes by analyzing and quantifying recipe features like color, flavor, and ingredients; and the menu optimization algorithm, which adjusts ingredient quantities to adhere to budget and nutritional standards. To evaluate the system's effectiveness, satisfaction surveys were conducted among school lunch menu planners, using a seven-point Likert scale to measure feedback on menu variety and cost-effectiveness. Results showed that the system aligns well with nutritional and budgetary requirements, with minimal errors in nutritional content and ingredient costs, and substantially reduces menu planning time.