Three-dimensional ant colony optimization for multi-compartment vehicle routing: Enhancing efficiency and reducing costs

Efficient routing of multi-compartment vehicles is a critical challenge in logistics, as it involves optimizing travel distance and load distribution while considering multiple constraints. Traditional vehicle routing algorithms often fail to address the complexities of compartmentalized cargo, leading to inefficiencies in delivery operations and increased costs. This study aims to bridge this gap by introducing a Three-Dimensional Ant Colony Algorithm (3D-ACA) for optimizing Multi-Compartment Vehicle Routing (MCVR). This research aims to minimize total travel distance while ensuring proper allocation of goods to vehicle compartments and adherence to delivery time constraints. The approach involves formulating the Multi-Compartment Vehicle Routing Problem (MCVRP) as an optimization model and applying 3D-ACA to generate efficient routing solutions. Unlike conventional methods, which primarily focus on shortest-path algorithms, this study incorporates three key factors, route efficiency, compartment constraints, and time windows, into a single optimization framework. The novelty of this research lies in the three-dimensional adaptation of the ant colony algorithm, which extends the standard routing problem to include compartment-based decision-making. This approach provides a more realistic and practical solution for logistics companies aiming to improve fleet utilization, reduce costs, and enhance operational efficiency. The proposed 3D-ACA significantly improved key performance metrics, including a 15.6% reduction in travel distance compared to Ant Colony Optimization (ACO), up to 17% lower operational costs, and over 95% feasibility rates across datasets. It also demonstrated superior scalability by solving large-scale problems (150 nodes) under 45 minutes, outperforming traditional methods such as GA and MIP in efficiency and constraint satisfaction.