  Tandem queueing systems are widely-used stochastic models that arise from
many real-life service operations systems. Motivated by the desire to
understand the trade-off between the performance and complexity of policies for
capacity-constrained tandem queueing systems, we investigate the long-run
expected time-average revenue, the gain, of the service provider for various
pricing policies. The gain-maximization problem is formulated as a Markov
decision process model but the optimal policy, which dynamically adjusts
service prices, is hard to obtain due to the curse of dimensionality. For
general tandem queueing systems, rather than identifying an optimal dynamic
policy, we show that the best possible static policy that quotes the same price
to all customers is asymptotically optimal when the buffer size at the first
station is sufficiently large. A noteworthy feature of our analysis is that we
identify an easy-to-obtain but asymptotic optimal static policy associated with
a simple optimization problem. We validate our analytic results through
numerical experiments and learn that, surprisingly, the gain under the simple
static policy is close to the optimal gain even when the buffer size at the
first station is moderate.
