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We study the problem of selecting a fleet of robots to service spatiallydistributed tasks with diverse requirements within time-windows. The problem ofallocating tasks to a fleet of potentially heterogeneous robots and finding anoptimal sequence for each robot is known as multi-robot task assignment (MRTA).Most state-of-the-art methods focus on the problem when the fleet of robots isfixed. In contrast, we consider that we are given a set of available robottypes and requested tasks, and need to assemble a fleet that optimally servicesthe tasks while the cost of the fleet remains under a budget limit. Wecharacterize the complexity of the problem and provide a Mixed-Integer LinearProgram (MILP) formulation. Due to poor scalability of the MILP, we propose aheuristic solution based on a Large Neighbourhood Search (LNS). In simulations,we demonstrate that the proposed method requires substantially lower budgetsthan a greedy algorithm to service all tasks.

Designing Heterogeneous Robot Fleets for Task Allocation and Sequencing