On the accuracy of the simple peak hour approximation for Markovian queues
Management Science
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Peak congestion in multi-server service systems with slowly varying arrival rates
Queueing Systems: Theory and Applications
Optimal Policies of Yield Management with Multiple Predetermined Prices
Operations Research
Monotone Optimal Policies for a Transient Queueing Staffing Problem
Operations Research
Transient and periodic solution to the time-inhomogeneous quasi-birth death process
Queueing Systems: Theory and Applications
Optimal admission and pricing control problem with deterministic service times and sideline profit
Queueing Systems: Theory and Applications
Dynamic pricing to control loss systems with quality of service targets
Probability in the Engineering and Informational Sciences
Effects of system parameters on the optimal policy structure in a class of queueing control problems
Queueing Systems: Theory and Applications
Dynamic pricing and scheduling in a multi-class single-server queueing system
Queueing Systems: Theory and Applications
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We consider congestion control in a nonstationary queueing system. Assuming that the arrival and service rates are bounded, periodic functions of time, a Markov decision process (MDP) formulation is developed. We show under the infinite horizon discounted and average reward optimality criteria, for each fixed time, optimal pricing and admission control strategies are nondecreasing in the number of customers in the system. This extends stationary results to the nonstationary setting. Despite this result, the problem still seems intractable. We propose an easily implementable pointwise stationary approximation (PSA) to approximate the optimal policies, suggest a heuristic to improve the implementation of the PSA and verify its usefulness via a numerical study.