Download PDF: Discrete Event System Simulation (Fifth Edition) by Banks et al. - A Comprehensive Textbook on DESS
Discrete event system simulation (DESS) is a powerful and widely used technique for modeling and analyzing complex systems that involve discrete events, such as arrivals, departures, failures, repairs, decisions, etc. DESS can help understand the behavior and performance of such systems, evaluate different scenarios and policies, and optimize their design and operation.
discrete event system simulation fifth pdf download
One of the most popular and comprehensive textbooks on DESS is "Discrete Event System Simulation" by Jerry Banks, John S. Carson II, Barry L. Nelson, and David M. Nicol. The fifth edition of this book was published in 2010 by Pearson Education. It covers both the theoretical foundations and practical applications of DESS, with numerous examples, exercises, case studies, and software tools.
If you are interested in learning more about DESS or using it for your research or teaching purposes, you might want to download the pdf version of this book for free. In this article, we will explain what DESS is and why it is important, what are the main features of the fifth edition of the book, and how to download it for free from reliable sources.
Discrete Event System Simulation: Concepts and Applications
A discrete event system is a system that changes its state only at discrete points in time, usually as a result of some events. For example, a queueing system changes its state when customers arrive or depart, a manufacturing system changes its state when machines start or finish processing jobs, a computer network changes its state when packets are transmitted or received, etc.
A discrete event system simulation (DESS) is a computer-based model that mimics the behavior and evolution of a real or hypothetical discrete event system over time. A DESS model consists of entities (objects that have attributes and interact with each other), events (occurrences that change the state of entities or trigger other events), activities (processes that consume time and resources), resources (elements that are required by activities), queues (places where entities wait for resources or events), random variables (elements that introduce uncertainty into the model), statistics (measures that summarize the output of the model), and animation (visual representation of the model).
The advantages of DESS include:
It can capture the complexity and dynamics of real systems that are difficult to analyze analytically or experimentally.
It can provide insights into the behavior and performance of systems under different conditions and scenarios.
It can support decision making and optimization by comparing alternative designs, policies, strategies, etc.
It can facilitate communication and learning among stakeholders involved in system design, operation, management, etc.
The challenges of DESS include:
It requires a clear and accurate definition of the system objectives, boundaries, assumptions, and data sources.
It involves a trade-off between model simplicity and realism, as well as between model validity and credibility.
It requires appropriate methods and tools for model development, verification, validation, experimentation, analysis, and presentation.
It depends on the skills and experience of the modeler and the user.
Some examples of DESS applications in different domains are:
Manufacturing: DESS can be used to model and optimize production systems, such as assembly lines, job shops, flexible manufacturing systems, etc.
Service: DESS can be used to model and optimize service systems, such as call centers, hospitals, banks, airports, etc.
Logistics: DESS can be used to model and optimize logistics systems, such as inventory systems, supply chains, transportation networks, etc.
Computer: DESS can be used to model and optimize computer systems, such as computer networks, distributed systems, parallel systems, etc.
Social: DESS can be used to model and analyze social systems, such as epidemics, urban dynamics, crowd behavior, etc.
Discrete Event System Simulation: The Fifth Edition
The fifth edition of the book "Discrete Event System Simulation" by Banks et al. is an updated and expanded version of the previous editions. It reflects the latest developments and trends in the field of DESS, such as agent-based simulation, web-based simulation, parallel and distributed simulation, etc. It also incorporates feedback from instructors and students who have used the book in their courses.
The book consists of 12 chapters that cover the following topics:
Introduction to simulation: This chapter introduces the basic concepts and terminology of simulation, the advantages and disadvantages of simulation, the steps of a simulation study, types of simulation models, simulation software packages, etc.
Simulation examples: This chapter presents some examples of simulation models in different domains, such as manufacturing, service, logistics, computer, etc. It also illustrates how to use some popular simulation software packages, such as Arena, Simio, ExtendSim, etc.
General principles: This chapter discusses some general principles and techniques for developing simulation models, such as conceptual modeling, data collection and analysis, random number generation, random variate generation, input modeling, verification and validation, output analysis, etc.
Simulation software: This chapter provides an overview of the features and capabilities of various simulation software packages available in the market. It also compares and contrasts different types of simulation software packages based on their characteristics and applications.
Discrete Event System Simulation: The Fifth Edition (continued)
Queueing models: This chapter introduces some basic concepts and models of queueing theory, such as arrival processes, service processes, queue disciplines, performance measures, etc. It also shows how to use simulation to analyze queueing systems and compare different queueing models.
Random-number generation: This chapter explains how to generate random numbers that are uniformly distributed over a given interval. It covers topics such as linear congruential generators, tests for randomness, techniques for improving random-number generation, etc.
Random-variate generation: This chapter explains how to generate random variates that follow a given probability distribution. It covers topics such as inverse transform technique, acceptance-rejection technique, special techniques for specific distributions, etc.
Input modeling: This chapter discusses how to select and fit probability distributions to represent the input data of a simulation model. It covers topics such as data collection and summary, goodness-of-fit tests, parameter estimation methods, etc.
Verification and validation of simulation models: This chapter discusses how to ensure that a simulation model is correct and credible. It covers topics such as verification techniques, validation techniques, face validity, sensitivity analysis, etc.
Output analysis for a single model: This chapter discusses how to analyze the output data generated by a single simulation run or multiple independent runs. It covers topics such as types of output data, terminating and steady-state simulations, point and interval estimation, output analysis for terminating simulations, output analysis for steady-state simulations, etc.
Comparison and evaluation of alternative system designs: This chapter discusses how to use simulation to compare and evaluate different system designs or policies. It covers topics such as comparison of two systems by confidence intervals, comparison of several systems by ranking and selection, comparison of several systems by multiple comparisons with the best, metamodeling, optimization via simulation, etc.
The book also includes several pedagogical features and resources that enhance the learning experience of the readers. These include:
Learning objectives: Each chapter begins with a list of learning objectives that summarize the main topics and skills covered in the chapter.