Enhanced Ant Colony Optimization for Capacitated Vehicle Routing Problem with Time Windows in Franchise Distribution

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.