  Autonomous mobile robots enable increased flexibility of manufacturing
systems. The design and operating strategy of such a fleet of robots requires
careful consideration of both fixed and operational costs. In this paper, a
Monte-Carlo Tree Search (MCTS)-based metaheuristic is developed that guides a
Branch & Bound (B&B) algorithm to find the globally optimal solution to the
Fleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW).The
metaheuristic and exact algorithms are implemented in a parallel hybrid
optimization algorithm where the metaheuristic rapidly finds feasible solutions
that provide candidate upper bounds for the B&B algorithm. The MCTS
additionally provides a candidate fleet composition to initiate the B&B search.
Experiments show that the proposed approach results in significant improvements
in computation time and convergence to the optimal solution.
