Integration of sustainable diet planning problem under neutrosophic fuzzy multi-objective optimization

• Published in: Expert systems with applications Vol. 297; p. 129360
• Main Authors: Divya, Kumari, Kaur, Prabjot
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2026
• Subjects: Intuitionistic; Multi-objective optimization; Neutrosophic fuzzy programming; Sustainable diet plan; Multi-objective optimization; Neutrosophic fuzzy programming; Intuitionistic; Sustainable diet plan
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2025.129360

•The work applies neutrosophic programming to model uncertainty in sustainable diabetic diet planning.•Integrates intuitionistic fuzzy and neutrosophic methods for multi-objective optimization.•Develops a novel diet model capturing truth, indeterminacy, and falsity in nutrition constraints.•Promotes sustainable, nutritionally adequate, affordable and eco-friendly dietary choices.•Validated through a case study demonstrating personalized and robust diet solutions. Graphical abstract of the Sustainable Diet Planning Problem (SDPP) framework. It outlines the integration of nutritional and environmental inputs into a multi-objective model, solved using Intuitionistic Fuzzy and Neutrosophic Fuzzy Programming approaches, with evaluation based on comparative analysis, sensitivity testing, and statistical validation. [Display omitted] Sustainable diet planning for diabetic individuals requires a complex trade-off among nutritional adequacy, cultural preferences, affordability, and environmental impact under uncertainty. Existing optimization approaches, including intuitionistic fuzzy sets (IFS), capture acceptance and rejection but fail to model indeterminacy, which is an important aspect of real-world dietary decisions. This study proposes a Neutrosophic Fuzzy Multi-objective Optimization (NFO) framework that incorporates truth, indeterminacy, and falsity membership functions to handle hesitancy in nutritional planning. The model optimizes the six conflicting objectives, maximizing the intake of fiber, protein, and carbohydrates and minimizing fat, sugar, and cost, subject to constraints aligned with Indian Council of Medical Research (ICMR) guidelines. A case study involving 11 common Indian food items across different age and gender groups validates the proposed framework. Comparative analysis with an IFS-based model and two variants of the NFO model reveals that NFO Model II consistently yields more balanced and robust diet plans across demographic groups. The proposed approach offers a computationally efficient and adaptable model for personalized diabetic meal planning, with broader implications for public health nutrition and sustainable food policy.