Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essentials of the Monte Carlo discrete-event simulation methodology, and does so in the context of a popular Arena simulation environment.” It treats simulation modeling as an in-vitro laboratory that facilitates the understanding of complex systems and experimentation with what-if scenarios in order to estimate their performance metrics. The book contains chapters on the simulation modeling methodology and the underpinnings of discrete-event systems, as well as the relevant underlying probability, statistics, stochastic processes, input analysis, model validation and output analysis. All simulation-related concepts are illustrated in numerous Arena examples, encompassing production lines, manufacturing and inventory systems, transportation systems, and computer information systems in networked settings.
· Introduces the concept of discrete event Monte Carlo simulation, the most commonly used methodology for modeling and analysis of complex systems · Covers essential workings of the popular animated simulation language, ARENA, including set-up, design parameters, input data, and output analysis, along with a wide variety of sample model applications from production lines to transportation systems · Reviews elements of statistics, probability, and stochastic processes relevant to simulation modeling
- Ample end-of-chapter problems and full Solutions Manual
- Includes CD with sample ARENA modeling programs
Junior and Senior undergraduate students taking required courses in “Simulation,” and related courses in “Simulation and Modeling,” and “Modeling and Analysis,” often mandated for majors in Industrial and Mechanical engineering programs, as well as offered for students in other engineering disciplines, including civil, electrical, and chemical engineering, among others. Graduate students in business programs taking courses in industrial management, and related areas in quality control, modeling of complex systems.
Chapter 1 Introduction to Simulation Modeling 1.1 Systems and Models 1.2 Analytical Versus Simulation Modeling 1.3 Simulation Modeling and Analysis 1.4 Simulation Worldviews 1.5 Model Building 1.6 Simulation Costs and Risks 1.7 Example: A Production Control Problem 1.8 Project Report Exercises Chapter 2 Discrete Event Simulation 2.1 Elements of Discrete Event Simulation 2.2 Examples of DES Models 2.2.1 Single Machine 2.2.2 Single Machine with Failures 2.2.3 Single Machine with an Inspection Station and Associated Inventory 2.3 Monte Carlo Sampling and Histories 2.3.1 Example: Work Station Subject to Failures and Inventory Control 2.4 DES Languages Exercises Chapter 3 Elements of Probability and Statistics 3.1 Elementary Probability Theory 3.1.1 Probability Spaces 3.1.2 Conditional Probabilities 3.1.3 Dependence and Independence 3.2 Random Variables 3.3 Distribution Functions 3.3.1 Probability Mass Functions 3.3.2 Cumulative Distribution Functions 3.3.3 Probability Density Functions 3.3.4 Joint Distributions 3.4 Expectations 3.5 Moments 3.6 Correlations 3.7 Common Discrete Distributions 3.7.1 Generic Discrete Distribution 3.7.2 Bernoulli Distribution 3.7.3 Binomial Distribution 3.7.4 Geometric Distribution 3.7.5 Poisson Distribution 3.8 Common Continuous Distributions 3.8.1 Uniform Distribution 3.8.2 Step Distribution 3.8.3 Tria
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- © Academic Press 2007
- 22nd June 2007
- Academic Press
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Professor Department of Industrial and Systems Engineering, Rutgers University, New Jersey
Professor, Department of Management Science and Information Systems, Rutgers Business School, Rutgers University, New Jersey