Most complex missions comprise of spatially separated tasks which have to be finished using teams of mobile robots. The main challenges for planning such missions are forming effective coalitions among available robots and assigning them to tasks in such a way that the expected mission completion time is minimized. Our model allows task execution by a fraction of the assigned team even when the rest of the team has not yet arrived at the task location. We also allow tasks to be interrupted and robots of assigned teams to be rescheduled from an unfinished task to another task. We describe five different heuristic algorithms to compute schedules for all robots assigned to the mission. We compare them and analyze the computational performance of the best performing strategy. We also show how to handle uncertainty that may arise during traveling or task execution and then study the effect of varying uncertainty on the minimization of mission completion time.

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