An Improved Ant Colony Optimization Algorithm for Vehicle Routing Problem with Time Windows

Distribution plays an important role in the supply chain system. One of the critical problems in distribution is the vehicle routing problem. This research proposes the Improved Ant Colony Optimization (IACO) algorithm to solve the Vehicle Routing Problem with Time Windows (VRPTW). The main objective is to minimize the total vehicle mileage by considering the vehicle capacity and customer time windows. The proposed IACO algorithm is inspired by the conventional Ant Colony Optimization (ACO) algorithm by adding local search and mutation processes. Numerical experiments were conducted to test that the routes generated did not violate the customer's time window constraints. In addition, this study also compares the proposed IACO algorithm routes with other metaheuristic algorithms, namely ACO classic and Tabu Search. In addition, this investigation was carried out by experimenting with the number of iterations. The results of numerical experiments prove that the proposed IACO algorithm can minimize the total vehicle mileage without violating capacity constraints and time windows.