The Vehicle Routing Problem (VRP) has come a long way from its roots in the 1960s Clarke-Wright savings heuristic. While Clarke-Wright is still useful for generating quick, reasonably good routes, it often struggles with complex constraints like tight delivery windows or mixed fleets. Modern metaheuristics—Tabu Search and Genetic Algorithms—pick up where classical heuristics leave off. Tabu Search systematically explores the solution space while avoiding loops, making it great for large instances that need refinement beyond an initial heuristic. Genetic Algorithms, by contrast, rely on “evolutionary” operations (selection, crossover, mutation) to diversify the search and escape local optima, which can be particularly effective for highly nonlinear cost structures.

More recently, Reinforcement Learning (RL) has entered the scene. Instead of hand-crafting neighborhood moves or recombination rules, RL trains an agent to build routes from scratch by rewarding low-cost solutions. Early studies show promise—especially for real-time re-routing when traffic or demand changes on the fly—but RL still requires massive training data and careful reward shaping to outperform traditional metaheuristics.

In practice, many logistics teams blend these approaches: a fast heuristic like Clarke-Wright or a greedy insertion builds a seed solution, then Tabu Search or a Genetic Algorithm polishes it, and RL (or a learned model) handles last-minute disruptions. What combinations have you tried in production, and which constraints (time windows, vehicle capacities, driver shifts) gave you the most trouble?