Task allocation in multi-robot Search and Rescue (SaR) is challenging: the environment is unknown, time-critical, and too dynamic for continuous centralized planning. This paper asks how a lightweight supervisory framework can dynamically allocate sub-tasks within a multi-robot team to balance exploration and victim retrieval without incurring prohibitive computational costs. We propose an event-driven, auction-based supervisory layer that intervenes only on discrete triggers; spatial conflicts, agent stagnation, and victim discovery, leaving robots to operate autonomously otherwise. Compared to a purely decentralized baseline across 216 simulated missions, the supervisor raises the victim discovery success rate from 50.5% to 91.7%, locates all victims roughly 24% faster without meaningfully increasing computation time. When extended to a heterogeneous fleet including a dedicated rescue agent, these exploration gains persist, but the increased rate of victim discovery outpaces single-agent rescue capacity, revealing a trade-off between exploration efficiency and victim retrieval throughput that the current allocation mechanism does not yet resolve. ... Read more