Operational efficiency is a critical determinant of organizational success, particularly in transportation, logistics, and supply chain management. The Vehicle Routing Problem (VRP) serves as a fundamental framework for optimizing these operations. This paper introduces a novel Swarm Intelligence-Based (SIB) algorithm designed to address large-scale, asymmetric datasets. Unlike traditional methods that primarily focus on minimizing route length, our approach optimizes travel time, incorporating per-vehicle constraints to ensure no vehicle exceeds its operational limits while simultaneously minimizing the total number of vehicles required. By decomposing the VRP into smaller Traveling Salesman Problem (TSP) subproblems, the algorithm enables simultaneous local and global optimization, leading to a more efficient and scalable solution process. Experimental results demonstrate that the proposed SIB algorithm outperforms existing methods in real-world applications, providing a robust framework for enhancing operational efficiency across various industries.