Linear Optimization for the Perfect Meal: A Data-Driven Approach to Optimising the Perfect Meal Using Gurobi

TL;DR

This paper presents a linear programming model using Gurobi to optimize meal plans for cost and nutrition, incorporating complex constraints for personalized, balanced diets.

Contribution

It introduces a comprehensive linear optimization framework for meal planning that accounts for cost, nutritional requirements, and additional constraints, enhancing practical dietary solutions.

Findings

Optimized meal plans meet nutritional and cost goals.

Model handles fractional weights and nutrient ratios effectively.

Case studies demonstrate real-world applicability.

Abstract

This study aims to optimize meal planning for nutritional health and cost efficiency using linear programming. Linear optimization provides an effective framework for addressing the problem of an optimal diet, as the composition of food can be naturally modeled as a linearly additive system. Leveraging a comprehensive nutrition dataset, our model minimizes meal costs while meeting specific nutritional requirements. We explore additional complexities, such as fractional weights and nutrient ratio constraints, enhancing the robustness of the solution. Case studies address common nutritional challenges, providing tailored diet plans. The significance lies in aiding individuals to form balanced, cost-effective dietary schedules, considering fitness goals and caloric needs. This research contributes to efficient, sustainable, and time-sensitive meal planning, emphasizing the intersection of nutrition, optimization, and real-world applicability.