Online Bipartite Matching with Reusable Resources

We study the classic online bipartite matching problem with a twist: offline nodes are reusable any number of times. Every offline node $i$ becomes available $d$ steps after it was assigned to. Nothing better than a $0.5$-approximation, obtained by the trivial deterministic greedy algorithm, was known for this problem. We give the first approximation factor beating $0.5$, namely a $0.505$ approximation, by suitably adapting and interpreting the powerful technique of Online Correlated Selection.