Discrete-Event Simulation
A First Course
Published on 25. January 2006
Book
Hardback
608 pages
978-0-13-142917-8 (ISBN)
Description
For advanced undergraduate and graduate courses in System Simulation or Simulation and Modeling.
This text introduces computational and mathematical techniques for modeling, simulating, and analyzing the performance of various systems. Its goal is to help students gain a better understanding of how systems operate and respond to change by: 1) helping them begin to model, simulate, and analyze simple-but- representative systems as soon as possible; and 2) whenever possible, encourage the experimental exploration and self-discovery of theoretical results before their formal presentation. The authors' approachable writing style emphasizes concepts and insight without sacrificing rigor.
This text introduces computational and mathematical techniques for modeling, simulating, and analyzing the performance of various systems. Its goal is to help students gain a better understanding of how systems operate and respond to change by: 1) helping them begin to model, simulate, and analyze simple-but- representative systems as soon as possible; and 2) whenever possible, encourage the experimental exploration and self-discovery of theoretical results before their formal presentation. The authors' approachable writing style emphasizes concepts and insight without sacrificing rigor.
More details
Content
1. Models
1.1. Introduction
1.2. A Single-Server Queue
1.3. A Simple Inventory System
2. Random Number Generation
2.1. Lehmer Random Number Generation: Introduction
2.2. Lehmer Random Number Generation: Implementation
2.3. Monte Carlo Simulation
2.4. Monte Carlo Simulation Examples
3. Discrete-Event Simulation
3.1. Discrete-Event Simulation
3.2. Multi-Stream Lehmer Random Number Generation
3.3. Discrete-Event Simulation Models
4. Statistics
4.1. Sample Statistics
4.2. Discrete-Data Histograms
4.3. Continuous-Data Histograms
4.4. Correlation
5. Next-Event Simulation
5.1. Next-Event Simulation
5.2. Next-Event Simulation Examples
5.3. Event List Management
6. Discrete Random Variables
6.1. Discrete Random Variables
6.2. Generating Discrete Random Variables
6.3. Discrete Random Variable Applications
6.4. Discrete Random Variable Models
6.5. Random Sampling
7. Continuous Random Variables
7.1. Continuous Random Variables
7.2. Generating Continuous Random Variables
7.3. Continuous Random Variable Applications
7.4. Continuous Random Variable Models
7.5. Nonstationary Poisson Processes
7.6. Acceptance-Rejection
8. Input Modeling
8.1. Error in Discrete-Event Simulation
8.2. Modeling Stationary Processes
8.3. Modeling Nonstationary Processes
9. Output Analysis
9.1. Interval Estimation
9.2. Monte Carlo Estimation
9.3. Finite-Horizon and Infinite-Horizon Statistics
9.4. Batch Means
9.5. Steady-State Single-Server Service Node Statistics
10. Projects
10.1. Empirical Tests of Randomness
10.2. Birth-Death Processes
10.3. Finite-State Markov Chains
10.4. A Network of Single-Server Service Nodes
Appendices:
A. Simulation Languages
B. Integer Arithmetic
C. Parameter Estimation Summary
D. Random Variate Models
E. Random Variate Generators
F. Correlation and Independence
References
1.1. Introduction
1.2. A Single-Server Queue
1.3. A Simple Inventory System
2. Random Number Generation
2.1. Lehmer Random Number Generation: Introduction
2.2. Lehmer Random Number Generation: Implementation
2.3. Monte Carlo Simulation
2.4. Monte Carlo Simulation Examples
3. Discrete-Event Simulation
3.1. Discrete-Event Simulation
3.2. Multi-Stream Lehmer Random Number Generation
3.3. Discrete-Event Simulation Models
4. Statistics
4.1. Sample Statistics
4.2. Discrete-Data Histograms
4.3. Continuous-Data Histograms
4.4. Correlation
5. Next-Event Simulation
5.1. Next-Event Simulation
5.2. Next-Event Simulation Examples
5.3. Event List Management
6. Discrete Random Variables
6.1. Discrete Random Variables
6.2. Generating Discrete Random Variables
6.3. Discrete Random Variable Applications
6.4. Discrete Random Variable Models
6.5. Random Sampling
7. Continuous Random Variables
7.1. Continuous Random Variables
7.2. Generating Continuous Random Variables
7.3. Continuous Random Variable Applications
7.4. Continuous Random Variable Models
7.5. Nonstationary Poisson Processes
7.6. Acceptance-Rejection
8. Input Modeling
8.1. Error in Discrete-Event Simulation
8.2. Modeling Stationary Processes
8.3. Modeling Nonstationary Processes
9. Output Analysis
9.1. Interval Estimation
9.2. Monte Carlo Estimation
9.3. Finite-Horizon and Infinite-Horizon Statistics
9.4. Batch Means
9.5. Steady-State Single-Server Service Node Statistics
10. Projects
10.1. Empirical Tests of Randomness
10.2. Birth-Death Processes
10.3. Finite-State Markov Chains
10.4. A Network of Single-Server Service Nodes
Appendices:
A. Simulation Languages
B. Integer Arithmetic
C. Parameter Estimation Summary
D. Random Variate Models
E. Random Variate Generators
F. Correlation and Independence
References