Discrete-Event System Simulation / Edition 3by Jerry Banks, David M. Nicol, Barry L. Nelson, John S. II Carson
Pub. Date: 08/15/2000
This book provides a basic treatment of discrete-event simulation, one of the most widely used operations research and management science tools for dealing with system design in the presence of uncertainty. Proper collection and analysis of data, use of analytic techniques, verification and validation of models and the appropriate design of simulation experiments are… See more details below
This book provides a basic treatment of discrete-event simulation, one of the most widely used operations research and management science tools for dealing with system design in the presence of uncertainty. Proper collection and analysis of data, use of analytic techniques, verification and validation of models and the appropriate design of simulation experiments are treated extensively. Readily understandable to those having a basic familiarity with differential and integral calculus, probability theory and elementary statistics. Includes simulation in C++, the latest versions of the most widely used packages, and features of simulation output analysis software. Covers properties, modeling and random-variate generation from the lognormal distribution. Clarifies the difficult distinctions between terminating and steady-state simulation, and between within- and across-replication statistics. Contains up-to-date treatment of simulation of manufacturing and material handling systems. Emphasizes the hierarchical nature of computing systems, and how simulation techniques vary, depending on the level of abstraction. For readers wanting to learn more about system simulation.
- Publication date:
- Prentice Hall International Series in Industrial and Systems Engineering
- Edition description:
- Older Edition
- Product dimensions:
- 6.06(w) x 9.50(h) x 1.04(d)
Table of Contents
(NOTE: Each chapter concludes with Summary, References, and Exercises.)
I. INTRODUCTION TO DISCRETE-EVENT SYSTEM SIMULATION.
2. Simulation Examples.
3. General Principles.
4. Simulation Software.
II. MATHEMATICAL AND STATISTICAL MODELS.
6. Queueing Models.
III. RANDOM NUMBERS.
8. Random-Variate Generation.
IV. ANALYSIS OF SIMULATION DATA.
10. Verification and Validation of Simulation Models.
11. Output Analysis for a Single Model.
12. Comparison and Evaluation of Alternative System Designs.
13. Simulation of Manufacturing and Material Handling Systems.
14. Simulation of Computer Systems.
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