  This paper presents a novel fleet management strategy for battery-powered
robot fleets tasked with intra-factory logistics in an autonomous manufacturing
facility. In this environment, repetitive material handling operations are
subject to real-world uncertainties such as blocked passages, and equipment or
robot malfunctions. In such cases, centralized approaches enhance resilience by
immediately adjusting the task allocation between the robots. To overcome the
computational expense, a two-step methodology is proposed where the nominal
problem is solved a priori using a Monte Carlo Tree Search algorithm for task
allocation, resulting in a nominal search tree. When a disruption occurs, the
nominal search tree is rapidly updated a posteriori with costs to the new
problem while simultaneously generating feasible solutions. Computational
experiments prove the real-time capability of the proposed algorithm for
various scenarios and compare it with the case where the search tree is not
used and the decentralized approach that does not attempt task reassignment.
