Efficient routing for distributing goods to multiple franchisee locations requires optimization techniques capable of handling vehicle capacity limits, heterogeneous time windows, and operational constraints, making conventional brute-force or map-based approaches infeasible due to the NP-hard nature of the problem. This study presents an enhanced Ant Colony Optimization (ACO) algorithm for solving the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) in a franchisor–franchisee logistics setting. The proposed enhancement incorporates feasibility filtering to enforce capacity and time-window constraints during route construction and adaptive pheromone updating to improve convergence stability. Using real franchisee coordinates, demand values, and operational time windows, the experiments configured with α = 2, β = 1, ρ = 0.05, and a 150-iteration limit demonstrate that the enhanced ACO achieves a minimum total route distance of 46.90 km with zero variance across 10 simulations, indicating highly stable convergence. Comparative evaluation shows that the enhanced ACO improves route efficiency by 11.4% compared to standard ACO and 15.2% relative to a representative Genetic Algorithm baseline. Implemented in a web-based environment using JavaScript for visualization and Java for computation, the approach provides a practical decision-support tool for Indonesian franchise logistics. The algorithm exhibits an observed computational complexity of θ(n4), making it suitable for small to medium-scale distribution networks involving strict delivery time windows.
Rachmawati et al. (Thu,) studied this question.