A Multi-Objective Dynamic Neutrosophic Solid Transportation Model Under Iot-Driven Uncertainty

This research introduces an innovative Multi-objective Dynamic Neutrosophic Solid Transportation Model (MODNSTP) that integrates real-time uncertainty from IOT-enabled logistics systems. Transportation factors, including pricing, supply, and capacity, are articulated by Single-Valued Neutrosophic Numbers (SVNNs) to more effectively represent vagueness, indeterminacy and contradicting data. A neutrosophic ranking function is utilized to derive precise values for optimization. The model concurrently reduces cost, delay, and environmental effects over various time intervals. A revised Row-Column Reduction and MODI approach is suggested for allocation. Dynamic IoT inputs incessantly alter parameters over time. A numerical example demonstrates improved decision-making efficacy amid uncertainty. A comparative analysis of fuzzy and crisp models reveals the benefits of the proposed methodology. This methodology pertains to intelligent supply chains and the logistics of perishable goods. It provides a scalable solution for real-time, uncertainty-informed transportation planning.