A scalable model for Capacitated Vehicle Routing Problem with Pickup and Delivery under dynamic constraints using adaptive heuristic-based ant colony optimization

Abstract

This study addresses the Capacitated Vehicle Routing Problem with Pickup and Delivery (CVRPPD), a core challenge in urban logistics involving the optimization of vehicle routes under dynamic constraints. Traditional algorithms predominantly focus on static variables like distance, failing to account for real-world factors such as traffic congestion, adverse weather, and vehicle capacity limitations. To solve this problem, the Adaptive Heuristic-Based Ant Colony Optimization (AHB-ACO) algorithm was developed, incorporating these dynamic constraints into the routing optimization process. The AHB-ACO algorithm minimizes total travel costs while ensuring adherence to vehicle capacity limits and improving route safety. Simulation tests were conducted on datasets with 50, 100, and 200 customers to evaluate performance under varying levels of complexity. The results demonstrate that AHB-ACO outperforms traditional ACO, particularly in dynamic scenarios, achieving a total cost of 4155.82 with an execution time of 1639.68 seconds for the 200-customer dataset. The algorithm’s adaptive heuristic formula integrates distance, traffic congestion, and weather penalties, enabling the generation of safer and more realistic routes. These results are explained by the algorithm’s ability to dynamically adjust to constraints, ensuring robust performance in complex environments. The findings highlight AHB-ACO’s practical applicability in urban logistics, offering scalability and adaptability for real-world delivery and pickup challenges, especially in areas affected by fluctuating traffic and weather conditions