An improved ACO algorithm for the cold-chain logistics distribution problem with time window

This article introduces an enhanced ant colony optimization (ACO) approach for addressing the cold-chain logistics distribution challenge, which includes time window constraints and temperature requirements. The proposed method addresses the inefficiencies and slow convergence of traditional ACO by incorporating customer service time windows and calculating travel times using the Ichaua function, which accounts for time and speed variations. It constructs a cost-effective cold chain delivery model that adapts to traffic conditions, ensuring adherence to service windows. The model utilizes the Dijkstra algorithm for shortest path determination and integrates Levy flight for pheromone updates, along with insertion and exchange operations to refine the delivery routes. Empirical tests demonstrate the algorithm's effectiveness in balancing variable costs and reducing overall delivery expenses, offering a practical solution for cold chain logistics enterprises facing dynamic traffic scenarios.