Salesman Problem

The Traveling Salesman Problem (TSP) seeks the shortest route visiting all points exactly once and returning to the origin, a classic optimization challenge with numerous real-world applications in logistics, robotics, and beyond. Current research focuses on improving solution speed and scalability for large-scale problems, employing diverse approaches such as graph neural networks (GNNs), reinforcement learning (RL), and hybrid methods combining classical algorithms with machine learning. These advancements are driving progress in solving complex variants of the TSP, including those with moving targets, obstacles, multiple agents, and capacity constraints, leading to more efficient solutions for real-world logistical and routing problems.