Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of multi-agent path finding (MAPF), is crucial for high-level coordination in multi-agent systems, such as pattern formation by robot swarms. This paper studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large instances using centralized approaches with a small computation time, and (2) online scenario: executing unlabeled-MAPF, despite the timing uncertainties real robots face. For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm that takes an arbitrary initial target assignment and then repeats one-timestep path planning with target swapping. TSWAP can adapt to both offline and online scenarios. We empirically demonstrate that offline TSWAP is highly scalable, and provides near-optimal solutions while reducing runtime by orders of magnitude compared with existing approaches. In addition, we present the benefits of online TSWAP, such as delay tolerance, through real-robot demonstrations.