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Discrete-Event Simulation: A First Course
Larry H Leemis
Stephen K. Park

ISBN-10: 0131429175
ISBN-13: 9780131429178

Publisher: Prentice Hall
Copyright: 2006
Format: Cloth; 624 pp
Published: 12/27/2005

Suggested retail price: $140.00
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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.

  • Manageable presentation:
    • Organized into 10 chapters and 41 sections overall

    • All core sections are consistent with a 75-minute classroom lecture

    • Optional sections in the first nine chapters can be used in a classroom setting as supplemental lectures.

  • Computer science orientation—Emphasizes an algorithmic approach throughout.
  • C software provided as source code for running simulations developed in the text and for homework exercises—Eliminates need for students to do all their programming from scratch.
  • A significant, but secondary, emphasis on Monte Carlo simulation and its relation to static stochastic systems.
  • Detailed specifications provided in each section for a variety of discrete-event simulation projects—Designed to integrate much of the core material.
  • Approximately 400 exercises provided—Reinforce and extend material and encourage computational experimentation.
  • Instructors Solutions Manual—provides solutions to problems in the text.



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

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Pearson Higher Education offers special pricing when you choose to package your text with other student resources. If you're interested in creating a cost-saving package for your students contact your Pearson Higher Education representative.


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