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. Language: English