Best-compromise nutritional menus for childhood obesity

Abstract

Childhood obesity is an undeniable reality and has shown a rapid growth in many countries.Obesity at an early age not only increases the risks of chronic diseases but also produces a problem for the whole healthcare system. One way to alleviate this problem is to provide each patient with an appropriate menu that can be defined with a mathematical model. Existing mathematical models only partially address the objective and constraints of childhood obesity; therefore, the solutions provided are insufficient for health specialists to prepare nutritional menus for individual patients. This manuscript proposes a multiobjective mathematical programming model to aid healthy nutritional menu planning to prevent childhood obesity. This model enables a plan for combinations and amounts of food across different schedules and daily meals. This approach minimizes the major risk factors of childhood obesity (i.e., glycemic load and cholesterol intake).In addition, it considers the minimization of nutritional mismatch and total cost. The model is solved using a deterministic method and two metaheuristic methods. To complete this numerical study, test instances associated with children aged 4-18 years old were created. The quality of the solutions generated using the three methods was similar, but the metaheuristic methods provided solutions in less computational time. The numerical results indicate proper guidelines for personalized plans for individual children.

Intelligent Local Search Optimization Methods to Optimal Morocco Regime

“…V. Mierlo have considered nearly the identical case by substitution of the regime cost and the fossil fuel depletion minimization [13]. At [14], the authors suggest a multiobjective programming framework which delivers a nutritional program plan and minimizes glycemic load and cholesterol consumption, seen as the major causes of childhood overweight.…”