Discrete-Event System Simulation (5^th Ed.)
Pearson New International Edition

For junior- and senior-level simulation courses in engineering, business, or computer science.
While most books on simulation focus on particular software tools, Discrete-Event System Simulation examines the principles of modeling and analysis that translate to all such tools. This language-independent text explains the basic aspects of the technology, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments. It offers an up-to-date treatment of simulation of manufacturing and material handling systems, computer systems, and computer networks.

• I Introduction to Discrete-Event System Simulation
• Chapter 1 Introduction to Simulation
• Chapter 2 Simulation Examples
• Chapter 3 General Principles
• Chapter 4 Simulation Software
• II Mathematical and Statistical Models
• Chapter 5 Statistical Models in Simulation
• Chapter 6 Queueing Models
• III Random Numbers
• Chapter 7 Random-Number Generation
• Chapter 8 Random-Variate Generation
• IV Analysis of Simulation Data
• Chapter 9 Input Modeling
• Chapter 10 Verification and Validation of Simulation Models
• Chapter 11 Output Analysis for a Single Model
• Chapter 12 Comparison and Evaluation of Alternative System Designs
• V Applications
• Chapter 13 Simulation of Manufacturing and Material-Handling Systems
• Chapter 14 Simulation of Computer Networks
• Appendix
• Index

• Simulation of Communications Systems includes new material on simulation beta distribution, negative binomial distribution and non-stationary processes.
• Subset selection methods used for output analysis of several alternatives are discussed.
• Numerous solved examples enhance understanding of concepts.
• Abundant figures, tables and end-chapter exercises are provided.
• Application topics promote understanding of real-world uses.
• Interpretation of simulation software output explains how to use software tools correctly.
• Discussion of simple tools for complex input modeling problems develops more realistic valid models.