The book "Simulation Modeling and Analysis" by Averill M. Law and W. David Kelton is a comprehensive guide to the principles and techniques of simulation modeling. The second edition covers a wide range of topics, including: 1. **Basic Simulation Modeling**: Introduces the nature of simulation, systems, models, and discrete-event simulation. It includes detailed examples of simulating queueing systems and inventory systems using various programming languages. 2. **Modeling Complex Systems**: Explores advanced simulation techniques, such as list processing, the SIMLIB language, and simulations of complex systems like time-shared computer models and job-shop models. 3. **Simulation Software**: Discusses the classification and features of simulation software, including GPSS, SIMAN/Cinema, SIMSCRIPT II.5, and SLAM II, and provides a comparison of different simulation languages. 4. **Review of Basic Probability and Statistics**: Covers essential probability distributions, random number generators, and methods for generating random variates. 5. **Building Valid and Credible Simulation Models**: Focuses on principles of valid simulation modeling, verification of simulation programs, and statistical procedures for comparing real-world observations with simulation output. 6. **Selecting Input Probability Distributions**: Teaches techniques for selecting appropriate probability distributions, including parameter estimation and goodness-of-fit tests. 7. **Random-Number Generators**: Explains various types of random number generators and methods for testing their quality. 8. **Generating Random Variates**: Describes methods for generating both continuous and discrete random variates, including correlated random variates and arrival processes. 9. **Output Data Analysis for a Single System**: Discusses transient and steady-state behavior, statistical analysis of simulation output, and methods for estimating performance measures. 10. **Comparing Alternative System Configurations**: Introduces confidence intervals for comparing performance measures of different systems and ranking and selection procedures. 11. **Variance-Reduction Techniques**: Explains methods to reduce the variance in simulation output, such as common random numbers, antithetic variates, and control variates. 12. **Experimental Design and Optimization**: Covers factorial designs, response surfaces, and gradient estimation for optimization. 13. **Simulation of Manufacturing Systems**: Focuses on the application of simulation in manufacturing, including modeling system randomness and a case study of a metal-parts manufacturing facility. The book is supported by appendices, problems, and references, making it a valuable resource for students, researchers, and practitioners in operations research and management science.The book "Simulation Modeling and Analysis" by Averill M. Law and W. David Kelton is a comprehensive guide to the principles and techniques of simulation modeling. The second edition covers a wide range of topics, including: 1. **Basic Simulation Modeling**: Introduces the nature of simulation, systems, models, and discrete-event simulation. It includes detailed examples of simulating queueing systems and inventory systems using various programming languages. 2. **Modeling Complex Systems**: Explores advanced simulation techniques, such as list processing, the SIMLIB language, and simulations of complex systems like time-shared computer models and job-shop models. 3. **Simulation Software**: Discusses the classification and features of simulation software, including GPSS, SIMAN/Cinema, SIMSCRIPT II.5, and SLAM II, and provides a comparison of different simulation languages. 4. **Review of Basic Probability and Statistics**: Covers essential probability distributions, random number generators, and methods for generating random variates. 5. **Building Valid and Credible Simulation Models**: Focuses on principles of valid simulation modeling, verification of simulation programs, and statistical procedures for comparing real-world observations with simulation output. 6. **Selecting Input Probability Distributions**: Teaches techniques for selecting appropriate probability distributions, including parameter estimation and goodness-of-fit tests. 7. **Random-Number Generators**: Explains various types of random number generators and methods for testing their quality. 8. **Generating Random Variates**: Describes methods for generating both continuous and discrete random variates, including correlated random variates and arrival processes. 9. **Output Data Analysis for a Single System**: Discusses transient and steady-state behavior, statistical analysis of simulation output, and methods for estimating performance measures. 10. **Comparing Alternative System Configurations**: Introduces confidence intervals for comparing performance measures of different systems and ranking and selection procedures. 11. **Variance-Reduction Techniques**: Explains methods to reduce the variance in simulation output, such as common random numbers, antithetic variates, and control variates. 12. **Experimental Design and Optimization**: Covers factorial designs, response surfaces, and gradient estimation for optimization. 13. **Simulation of Manufacturing Systems**: Focuses on the application of simulation in manufacturing, including modeling system randomness and a case study of a metal-parts manufacturing facility. The book is supported by appendices, problems, and references, making it a valuable resource for students, researchers, and practitioners in operations research and management science.