| We consider two congestion control models—(i) pricing control and (ii) admission control—in a nonstationary queueing system. Assuming that the arrival and service rates are measurable, bounded, and periodic functions of time, a Markov decision process (MDP) formulation for each model is developed. For each fixed time, we show optimal pricing and admission control structures become more strict as the number of customers in the system increases under the infinite-horizon discounted and average reward optimality criteria. This monotone structure extends well-known stationary results to the nonstationary settings. Even with this insight, however, exact solution of the problem is intractable. We propose an easily implementable pointwise stationary approximation (PSA) to approximate the optimal policies in each model and suggest several PSA-based heuristics. Finally, their utility is verified via numerical studies. |
