Multiobjective Diabetic Diet Model Using Neutrosophic Fuzzy Programming

The global health issue of diabetes, influenced by sedentary lifestyles and nutrient-deficient diets, is addressed in this paper. The focus is optimizing and modeling the diabetic diet in a neutrosophic fuzzy environment. A multiobjective linear programming model is proposed to maximize fiber and carbohydrates, minimize fat and sugar, and adhere to daily nutrient requirements. However, relying solely on precise data for dietary recommendations overlooks the fluctuations and uncertainties in nutrient consumption. To address this, the fuzzy and intuitionistic concept accurately represents diet acceptance and rejection but fails to account for indeterminacy. To handle this indeterminacy in the data, the neutrosophic set theory is employed, which plays a vital role in simulating the decision-making process by considering all aspects of a decision—truth, indeterminacy, and falsity. The findings of this study indicate that utilizing neutrosophic optimization leads to the best optimal solution, increasing the satisfactory degree of decision-makers in terms of nutrient acceptance, conflicts, and non-acceptance. To illustrate the diet model in a neutrosophic fuzzy environment, a numerical example is provided, along with a comparative analysis with other approaches.