Construct, Merge, Solve & Adapt with Reinforcement Learning for the min-max Multiple Traveling Salesman Problem

Researchers developed RL-CMSA, a hybrid reinforcement learning approach for solving the min-max Multiple Traveling Salesman Problem that combines probabilistic clustering, exact optimization, and solution refinement. The method outperforms existing algorithms by balancing exploration and exploitation to minimize the longest tour across multiple salesmen.