A Hybrid Particle Swarm Optimization with Tabu Search for Optimizing Aid Distribution Route

Abstract

This paper explores the use of metaheuristic algorithms for the Multi-Depot Vehicle Routing Problem (MDVRP), a complex form of the Vehicle Routing Problem (VRP) crucial in logistics. The study contributes to operational research, offering strategies for effective logistics management and underscores the significance of metaheuristic algorithms in tackling intricate optimization problems. The study focuses on optimizing vehicle routes from multiple depots, using a k-clustering technique for initial grouping. It examines algorithms like Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and a hybrid of PSO with Tabu Search (PSO-TS). These algorithms are vital for efficient route planning in varied environments, with practical implications demonstrated in real-world logistics scenarios. The findings revealed the limitations of the PSO algorithm and showed the improvement with Tabu Search. While, the resulting hybrid, PSO-TS, demonstrated remarkable improvements and stands out for its efficiency and reliability in MDVRP, it underscored the potential of metaheuristic algorithms in solving NP-hard combinatorial problems.