Multi Depot

Multi-depot vehicle routing problems (MDVRPs) optimize the assignment of vehicles to multiple depots and the subsequent routing to serve customers, aiming to minimize total travel distance or time. Current research focuses on developing efficient algorithms, including integer linear programming (ILP), metaheuristics like genetic algorithms and biased random-key genetic algorithms, and increasingly, neural network-based approaches that learn to predict optimal routing costs or directly improve existing solutions. These advancements are crucial for optimizing logistics in various sectors, such as emergency response (using UAVs) and power grid maintenance, where efficient resource allocation across multiple bases is paramount.