Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search

This content addresses the scalability limitations of using exact optimization methods like Mixed Integer Linear Programs (MILP) for large vehicle routing problems. It introduces Adaptive Large Neighborhood Search (ALNS), a powerful metaheuristic, as an alternative for finding high-quality solutions to complex logistics challenges. The ALNS methodology works by iteratively destroying parts of a solution using operators like random or worst removal, and then repairing it with methods such as greedy or regret-2 insertion. The algorithm adaptively learns which destroy and repair operators are most effective by weighting them based on past performance and uses a simulated annealing approach to accept new solutions, helping it escape local optima.