This book, "Simulation Modeling and Analysis" by Averill M. Law and W. David Kelton, is a comprehensive guide to simulation modeling and analysis. It covers the fundamentals of simulation, including discrete-event simulation, queueing systems, inventory systems, and distributed simulation. The book also discusses the steps involved in a simulation study and explores various types of simulation, such as continuous simulation, combined discrete-continuous simulation, and Monte Carlo simulation. The text provides detailed examples of simulation models, including FORTRAN, Pascal, and C programs, and discusses the advantages, disadvantages, and pitfalls of simulation. It also includes appendices on queueing systems, computer hardware and compilers, and simulation languages. Chapter 2 focuses on modeling complex systems, introducing a simple simulation language called SIMLIB and demonstrating its use in various simulation scenarios. Chapter 3 discusses simulation software, comparing different simulation languages and their features. Chapter 4 reviews basic probability and statistics, essential for understanding simulation output data. Chapter 5 addresses the importance of building valid and credible simulation models, discussing verification and validation techniques. Chapter 6 covers the selection of input probability distributions, including techniques for assessing sample independence and fitting distributions to data. Chapters 7 through 13 delve into random-number generators, generating random variates, output data analysis, comparing system configurations, variance-reduction techniques, experimental design and optimization, and simulation of manufacturing systems. The book concludes with appendices, indexes, and references, providing a thorough resource for students and professionals in operations research, management science, and related fields.This book, "Simulation Modeling and Analysis" by Averill M. Law and W. David Kelton, is a comprehensive guide to simulation modeling and analysis. It covers the fundamentals of simulation, including discrete-event simulation, queueing systems, inventory systems, and distributed simulation. The book also discusses the steps involved in a simulation study and explores various types of simulation, such as continuous simulation, combined discrete-continuous simulation, and Monte Carlo simulation. The text provides detailed examples of simulation models, including FORTRAN, Pascal, and C programs, and discusses the advantages, disadvantages, and pitfalls of simulation. It also includes appendices on queueing systems, computer hardware and compilers, and simulation languages. Chapter 2 focuses on modeling complex systems, introducing a simple simulation language called SIMLIB and demonstrating its use in various simulation scenarios. Chapter 3 discusses simulation software, comparing different simulation languages and their features. Chapter 4 reviews basic probability and statistics, essential for understanding simulation output data. Chapter 5 addresses the importance of building valid and credible simulation models, discussing verification and validation techniques. Chapter 6 covers the selection of input probability distributions, including techniques for assessing sample independence and fitting distributions to data. Chapters 7 through 13 delve into random-number generators, generating random variates, output data analysis, comparing system configurations, variance-reduction techniques, experimental design and optimization, and simulation of manufacturing systems. The book concludes with appendices, indexes, and references, providing a thorough resource for students and professionals in operations research, management science, and related fields.