Optimizing transport routing in the supply chain using the Ant Colony Optimization in MATLAB

In recent years, numerous efforts have focused on lowering transportation costs through various vehicle routing problem models. The increasing expenses in transportation have led many to seek ways to cut these costs by implementing vehicle routing systems. This study explores the transportation routing problem within the supply chain. The vehicle routing problem involves vehicles leaving a warehouse, visiting a group of customers, and then returning to the warehouse. Each vehicle has a fixed capacity, and each customer has a specific demand that must be completely fulfilled. The challenge lies in selecting the customers for each vehicle and designing routes according to vehicle routing problem rules, aiming to minimize the total distance traveled, the overall early and late times, and ultimately the total cost. As the vehicle routing problem is intricate and falls under the NP-Hard category, metaheuristic methods are used to address it. This research suggests employing the Ant Colony Optimization to tackle the vehicle routing problem, with the objective of finding an optimal solution using MATLAB software.

Keywords:

Vehicle Routing , Ant Colony Optimization , Supply Chain , Optimization , Transportation , MATLAB Software , ACO , Cost , NP-Hard category , Metaheuristic methods , Solution , Supply chain