Motivated by problems in Bayesian reusable resource allocation, we introduce the concept of stationary online contention resolution schemes (S-OCRSs). OCRSs are a central tool used to solve non-reusable resource allocation problems. They convert solutions to fluid approximations of problems into feasible online policies while approximately preserving allocation probabilities. S-OCRSs depart from standard OCRSs in that they ensure that the probability of allocating any given set of resources is independent of the arrival order of requests. We show how S-OCRSs can be used to solve reusable resource allocation problems, and discuss a general 'maximum-entropy' approach to construct and analyze S-OCRSs. Our approach, using a unified method for a variety of feasibility constraints, obtains results that match the state-of-the-art for OCRSs, and even improves it for a bipartite matching feasibility constraint. Our results for reusable resource allocation also extend to the assortment optimization setting, and our policies can be implemented using prices.

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