  We consider Online Minimum Bipartite Matching under the uniform metric. We
show that Randomized Greedy achieves a competitive ratio equal to $(1+1/n)
(H_{n+1}-1)$, which matches the lower bound. Comparing with the fact that RG
achieves an optimal ratio of $\Theta(\ln n)$ for the same problem but under the
adversarial order, we find that the weaker arrival assumption of random order
doesn't offer any extra algorithmic advantage for RG, or make the model
strictly more tractable.
