This study proposes a novel neutrosophic fuzzy programming framework for solving multiobjective transportation problems (MOTPs) with a time-dependent survival cost function. The proposed model integrates parabolic fuzzy parameters with a neutrosophic compromise programming approach (NCPA) to manage uncertainty, indeterminacy, and imprecise information in decision-making. The inclusion of survival cost functions enables the model to capture the probabilistic nature of transportation reliability under fluctuating supply and demand conditions. The fuzzy model is transformed into an equivalent deterministic form using de-fuzzification, and the neutrosophic framework is subsequently applied to derive a compromise solution. A real-life case study in pharmaceutical logistics demonstrates the practical relevance of the model. It highlights its capability to generate more stable and balanced results compared to classical fuzzy programming. The findings suggest that neutrosophic programming provides a more comprehensive decision-support mechanism for MOTPs operating under uncertainty.
Khan et al. (2026) studied this question.