Overview

APPLIED SIMULATION MODELING provides the student with both a conceptual introduction to the concepts of simulation modeling and practical experience with real examples using popular commercial simulation packages ARENA and @Risk. The coverage includes Risk Simulation, Dynamic Systems, and Discrete Event Simulation models. Throughout the text, the authors show readers how they can use simulation in the context of decision making. Practical examples from Operations Management, Manufacturing, Health Care, and Finance are included throughout to give students an appreciation for the wide scope of application and the robust nature of simulation modeling. Special student editions of ARENA and @Risk are packaged with the text.

Full Product Details

Author:   Andrew Seila ,  Vlatko Ceric ,  Pandu Tadikamalla
Publisher:   Cengage Learning, Inc
Imprint:   Brooks/Cole
Edition:   New edition
Dimensions:   Width: 19.50cm , Height: 2.20cm , Length: 24.10cm
Weight:   0.908kg
ISBN:  

9780534381592

ISBN 10:   0534381596
Pages:   494
Publication Date:   26 February 2003
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

1. INTRODUCTION. Model Building and Decision Making: OR/MS Tools. Simulation as a Tool to Analyze Models. Overview of Simulation Models. 2. SIMULATION CONCEPTS AND SPREADSHEETS. Definition of Static Simulation. Several Examples. 3. FINANCIAL MODELS AND RISK ANALYSIS USING @RISK. An Insurance Model to Estimate Loss Ratio. A Model for Stock (or Bond) Valuation. Option Pricing. A Portfolio Model. 4. DYNAMIC SYSTEM MODELS. Definition of Dynamic Systems. Characteristics of Dynamic Simulations. Examples. 5. DISCRETE EVENT SIMULATION. Dynamic Structure: Events and Event Sequencing. Examples. Static Structure: Entities, Attributes, Lists. Examples. Model Verification. 6. SYSTEM MODELING PARADIGMS. What is a Simulation World View? Event View. Activity View. Process View. 7. ARENA AND VISUAL INTERACTIVE SIMULATION. Visual Interactive Simulation (VIS). Overview of Arena. A Simple Queuing System in Arena. The System Modeling Process with Arena. 8. PROBLEM SOLVING USING SIMULATION. Waiting Line Systems (Service Systems). Manufacturing Systems. 9. GRAPHICAL MODELING. Graphical Models. Advantages of Graphical Modeling. Graphical Modeling Techniques. Execution of Graphical Models. 10. SIMULATION SOFTWARE. Types of Simulation Software. Simulation Languages. Simulation Environments. Simulation Libraries. Simulation Software for Special Purposes. 11. CONSIDERATIONS IN LARGE-SCALE SIMULATION. The Nature of Real-World Models. The Simulation Process. The Team Approach and the Need for Expertise. Working with Clients. Data Collection and Measurement. Model Organization: Submodels. Model Development and Testing. Evolutionary Model Building. A Case Study.

Reviews

"1. INTRODUCTION. Model Building and Decision Making: OR/MS Tools. Simulation as a Tool to Analyze Models. Overview of Simulation Models. 2. SIMULATION CONCEPTS AND SPREADSHEETS. Definition of ""Static"" Simulation. Several Examples. 3. FINANCIAL MODELS AND RISK ANALYSIS USING @RISK. An Insurance Model to Estimate Loss Ratio. A Model for Stock (or Bond) Valuation. Option Pricing. A Portfolio Model. 4. DYNAMIC SYSTEM MODELS. Definition of Dynamic Systems. Characteristics of Dynamic Simulations. Examples. 5. DISCRETE EVENT SIMULATION. Dynamic Structure: Events and Event Sequencing. Examples. Static Structure: Entities, Attributes, Lists. Examples. Model Verification. 6. SYSTEM MODELING PARADIGMS. What is a Simulation World View? Event View. Activity View. Process View. 7. ARENA AND VISUAL INTERACTIVE SIMULATION. Visual Interactive Simulation (VIS). Overview of Arena. A Simple Queuing System in Arena. The System Modeling Process with Arena. 8. PROBLEM SOLVING USING SIMULATION. Waiting Line Systems (Service Systems). Manufacturing Systems. 9. GRAPHICAL MODELING. Graphical Models. Advantages of Graphical Modeling. Graphical Modeling Techniques. Execution of Graphical Models. 10. SIMULATION SOFTWARE. Types of Simulation Software. Simulation Languages. Simulation Environments. Simulation Libraries. Simulation Software for Special Purposes. 11. CONSIDERATIONS IN LARGE-SCALE SIMULATION. The Nature of Real-World Models. The Simulation Process. The Team Approach and the Need for Expertise. Working with Clients. Data Collection and Measurement. Model Organization: Submodels. Model Development and Testing. Evolutionary Model Building. A Case Study."

1. INTRODUCTION. Model Building and Decision Making: OR/MS Tools. Simulation as a Tool to Analyze Models. Overview of Simulation Models. 2. SIMULATION CONCEPTS AND SPREADSHEETS. Definition of Static Simulation. Several Examples. 3. FINANCIAL MODELS AND RISK ANALYSIS USING @RISK. An Insurance Model to Estimate Loss Ratio. A Model for Stock (or Bond) Valuation. Option Pricing. A Portfolio Model. 4. DYNAMIC SYSTEM MODELS. Definition of Dynamic Systems. Characteristics of Dynamic Simulations. Examples. 5. DISCRETE EVENT SIMULATION. Dynamic Structure: Events and Event Sequencing. Examples. Static Structure: Entities, Attributes, Lists. Examples. Model Verification. 6. SYSTEM MODELING PARADIGMS. What is a Simulation World View? Event View. Activity View. Process View. 7. ARENA AND VISUAL INTERACTIVE SIMULATION. Visual Interactive Simulation (VIS). Overview of Arena. A Simple Queuing System in Arena. The System Modeling Process with Arena. 8. PROBLEM SOLVING USING SIMULATION. Waiting Line Systems (Service Systems). Manufacturing Systems. 9. GRAPHICAL MODELING. Graphical Models. Advantages of Graphical Modeling. Graphical Modeling Techniques. Execution of Graphical Models. 10. SIMULATION SOFTWARE. Types of Simulation Software. Simulation Languages. Simulation Environments. Simulation Libraries. Simulation Software for Special Purposes. 11. CONSIDERATIONS IN LARGE-SCALE SIMULATION. The Nature of Real-World Models. The Simulation Process. The Team Approach and the Need for Expertise. Working with Clients. Data Collection and Measurement. Model Organization: Submodels. Model Development and Testing. Evolutionary Model Building. A Case Study.

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