The TIS Forest data structure, while effective for maintaining the shortest paths to goal safe intervals, may have limitations when handling larger-scale problems. One potential limitation is the scalability of the data structure as the number of agents or goals increases. As the size of the TIS Forest grows with the number of agents and goals, the computational and memory requirements may become prohibitive for very large-scale problems.
To address this limitation and improve the scalability of the TIS Forest, several enhancements can be considered. One approach is to implement hierarchical or distributed versions of the TIS Forest, where the data structure is organized into multiple levels or partitions to manage the complexity of larger problems more efficiently. By dividing the TIS Forest into smaller substructures, the algorithm can handle larger-scale problems more effectively.
Additionally, optimizing the data structure's search and update operations can improve its efficiency for larger-scale problems. Techniques such as pruning redundant paths, optimizing data storage, and parallelizing computations can enhance the performance of the TIS Forest in handling complex and extensive multi-agent pathfinding scenarios.