Optimization of a Lean Vendor–Buyer Supply Chain Model under Neutrosophic Fuzzy Environment with Transportation, Loading, and Unloading Considerations

Abstract

To analyze and justify the impact of automated truck loading system technology on minimizing lead time in warehouse loading and unloading processes for both vendor- buyer in the supply chain. A non-linear lean supply chain model is formulated for a single vendor–buyer system handling a single item, with the inclusion of freight forwarding services. The model explicitly accounts for transportation, loading, and unloading activities under two alternative loading technologies: automated truck loading systems and conventional forklift loading systems. In this framework, lead time is modeled as a function of production, loading and unloading, transportation, and in-transit durations. To reduce total lead time, automated truck loading system technology is incorporated, offering an advanced alternative to traditional forklift operations. Given the inherent uncertainty and variability in real-world supply chain environments, Single-valued Trapezoidal Neutrosophic fuzzy parameters are introduced to better capture imprecision in system parameters. To solve the formulated non-linear problem, the Lagrangian method is employed to derive the optimal solution, thereby enabling decision-makers to evaluate trade-offs between lead time reduction, efficiency, and system flexibility. The proposed model was solved using the prescribed method, and the results show that the total lead time with the incorporation of automated truck loading system technology is 5.834 days, whereas the total lead time with the forklift loading system is 10.46 days. This significant reduction in lead time demonstrates that the automated truck loading system substantially outperforms the conventional forklift loading system, thereby improving overall efficiency and responsiveness in the supply chain. From a managerial perspective, adopting automated loading technology can lead to significant improvements in supply chain efficiency, reduced operational delays, and enhanced responsiveness to customer demand.