We consider the problem of pricing a reusable resource service system. Potential customers arrive according to a Poisson process and purchase the service if their valuation exceeds the current price. If no units are available, customers immediately leave without service. Serving a customer corresponds to using one unit of the reusable resource, where the service time has an exponential distribution. This system is equivalent to the classical Erlang loss model. The objective is to maximize the steady-state revenue rate. Although an optimal policy is fully dynamic, we provide two main results that show a simple static policy is universally near-optimal for any service rate, arrival rate, and number of units in the system. When there is one class of customers who have a monotone hazard rate (MHR) valuation distribution, we prove that a static pricing policy guarantees 90.2% of the revenue from the optimal dynamic policy. When there are multiple classes of customers that each have their own regular valuation distribution and service rate, we prove that static pricing guarantees 78.9% of the revenue of the optimal dynamic policy. In this case, the optimal policy grows exponentially large in the number of classes while the static policy requires only one price per class.

翻译：我们研究可复用资源服务系统的定价问题。潜在顾客按照泊松过程到达，若其估价超过当前价格则购买服务。若无可用资源，顾客立即离开且不享受服务。服务一个顾客相当于占用一个可复用资源单元，服务时间服从指数分布。该系统等价于经典厄朗损失模型。目标是最大化稳态收益速率。尽管最优策略是完全动态的，我们提供两个主要结果表明，对于任何服务速率、到达速率和系统资源单元数，简单静态策略普遍接近最优。当存在一类估价服从单调风险率分布的顾客时，我们证明静态定价策略能保证达到最优动态策略收益的90.2%。当存在多类顾客，每类具有各自正则估价分布和服务速率时，我们证明静态定价能保证达到最优动态策略收益的78.9%。在此情形下，最优策略的复杂度随顾客类别数量呈指数增长，而静态策略每类仅需一个价格。