3 accepted papers
Combinatorial optimization problems involving multiple agents are notoriously challenging due to their NP-hard nature and the necessity for effective agent coordination. Despite advancements in learning-based methods, existing approaches often face critical limitations, including suboptimal agent co…
In this work, we propose a novel method, NLNS-MASPF, to solve the Multi-Agent Scheduling and Pathfinding (MASPF) problem. The problem exhibits a bi-level structure, consisting of High-level Scheduling and Low-level Pathfinding. Our method applies a graph neural network in the high-level scheduling p…
In this work, we consider the problem of allocating tasks and planning paths for multiple robots to operate cooper-atively. We formulate the problem as a bi-level optimization that involves optimizing the scheduling of robots and the collision-free path for each robot. To address the complexity of t…