Regeneration and Networks of Queues

1 Discrete Event Simulation.- 1.1 Methodological Considerations l.- 1.2 The Generalized Semi-Markov Process Model.- 1.3 Specification of Discrete Event Simulations.- 2 Regenerative Simulation.- 2.1 Regenerative Stochastic Processes.- 2.2 Properties of Regenerative Processes.- 2.3 The Regenerative Method for Simulation Analysis.- 2.4 Implementation Considerations.- 2.5 Theoretical Values for Discrete Time Markov Chains.- 2.6 Theoretical Values for Continuous Time Markov Chains.- 2.7 Efficiency of Regenerative Simulation.- 2.8 Regenerative Generalized Semi-Markov Processes.- 3 Markovian Networks.- 3.1. Markovian Job Stack Processes.- 3.2. Augmented Job Stack Processes.- 3.3. Irreducible, Closed Sets of Recurrent States.- 3.4. The Marked Job Method.- 3.5. Fully Augmented Job Stack Processes.- 3.6. The Labelled Jobs Method.- 3.7. Sequences of Passage Times.- 3.8. Networks with Multiple Job Types.- 3.9. Simulation for Passage Times.- 4 Non-Markovian Networks.- 4.1 Networks with Single States.- 4.2 Regenerative Simulation of Non-Markovian Networks.- 4.3 Single States for Passage Times.- 4.4 Recurrence and Regeneration.- 4.5 The Marked Job Method.- 4.6 Finite Capacity Open Networks.- 4.7 Passage Through Subnetworks.- 4.8 The Underlying Stochastic Structure.- 4.9 The Labelled Jobs Method.- 4.10 Comparison of Methods.- Appendix 1 Limit Theorems for Stochastic Processes.- Appendix 2 Convergence of Passage Times.- Symbol Index.