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Autonomous mobile robots enable increased flexibility of manufacturingsystems. The design and operating strategy of such a fleet of robots requirescareful consideration of both fixed and operational costs. In this paper, aMonte-Carlo Tree Search (MCTS)-based metaheuristic is developed that guides aBranch & Bound (B&B) algorithm to find the globally optimal solution to theFleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW). Themetaheuristic and exact algorithms are implemented in a parallel hybridoptimization algorithm where the metaheuristic rapidly finds feasible solutionsthat provide candidate upper bounds for the B&B algorithm. The MCTSadditionally provides a candidate fleet composition to initiate the B&B search.Experiments show that the proposed approach results in significant improvementsin computation time and convergence to the optimal solution.

A Parallel Monte-Carlo Tree Search-Based Metaheuristic For Optimal Fleet Composition Considering Vehicle Routing Using Branch & Bound