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This work presents a framework for multi-robot tour guidance in a partially known environment with uncertainty, such as a museum. A simultaneous matching and routing problem (SMRP) is formulated to match the humans with robot guides according to their requested places of interest (POIs) and generate the routes for the robots according to uncertain time estimation. A large neighborhood search algorithm is developed to efficiently find sub-optimal low-cost solutions for the SMRP. The scalability and optimality of the multi-robot planner are evaluated computationally. The largest case tested involves 50 robots, 250 humans, and 50 POIs. A photo-realistic multi-robot simulation was developed to verify the tour guiding performance in an uncertain indoor environment.