that reach their target given a time deadline. Algorithms Several algorithms have been proposed to solve the MAPF problem. The issue is that it is NP-hard to find optimal makespan or flow-time solutions, also when considering planar graphs, or graphs similar to grids. For what concerns bounded suboptimal solutions, it is shown that it is NP-hard to find a makespan-optimal solution with a factor of suboptimality smaller than . Optimal MAPF solvers return high quality solutions, but their efficiency is low. Instead, bounded-suboptimal and suboptimal solvers are more efficient, but their solutions are less effective. Also machine learning approaches have been proposed to solve the MAPF problem. Prioritized Planning One possible approach to face the computational complexity is prioritized planning. It consists in decoupling the MAPF problem into single-agent pathfinding problems. The first step is to assign to each agent a unique number that corresponds to the priority given to the agent. Then, following the priority order, for each agent a plan is computed to reach the target location. When planning, agents have to avoid collisions with paths of other agents with a higher priority that have already computed their plans. Finding a solution for the MAPF problem in such setting corresponds to the shortest path problem in a time-expansion graph. A time-expansion graph is a graph that takes into account the passing of time. Each node is composed by two entries , where is the node name and is the time step. Each node is linked to the nodes such that is adjacent to and is not occupied at time step . The drawback of prioritized planning is that, even if it is a sound approach (it returns valid solutions), it is neither optimal nor complete. This means that it is not assured that the algorithm will return a solution and, even in that case, the solution may not be optimal. Optimal MAPF Solvers It is possible to distinguish four different categories of optimal MAPF solvers: Extensions of A*: algorithms in this category employ modified versions of the A* approach. Increasing Cost Tree Search: a novel formalization of the MAPF problem is proposed, that comprehends an increasing search tree and the corresponding algorithm. The algorithm is composed by two levels and relies on the assumption that a valid solution for the MAPF problem is composed by a set of solutions for the single agents. Conflict-Based Search: this algorithm computes