Motivated by hotel housekeeping staffing challenges, we study a model in which reusable resources require service between intervals of use by successive customers. For each day, the firm decides how many servers to work a shift, and when each should begin working. Customers arrive and depart randomly over the course of the day. The firm's objective is to minimize the combined staffing cost and cost of guests waiting. We use a sample average approximation approach to solve this problem as an integer linear program and provide structural properties for a simple version of the problem. We test the resulting policy on a discrete-event simulation using both stylized and empirical hotel guest arrival and departure data. We show that the optimization methods based scheduling heuristics can save up to 17% of the total cost compared to a common industry staffing heuristic. With less or equal labor force than the common staffing, the schedule heuristics we propose can lower the guest wait time by up to 80% at a higher guest flow level and 50% at a lower level of guest flow. The optimization methods covered by this paper can be applied by hotel, airport, car rental managers to save the labor costs and improve service readiness.