What Every Engineer Should Know About Modeling and Simulation

Raymond J. Madachy, Daniel Houston

• Publisher: CRC Press
• Year: 2017
• eBook ISBN: 9781351648646
• Topic: Informatica
• Subtopic: Informatica generale

This practical book presents fundamental concepts and issues in computer modeling and simulation (M&S) in a simple and practical way for engineers, scientists, and managers who wish to apply simulation successfully to their real-world problems. It offers a concise approach to the coverage of generic (tool-independent) M&S concepts and enables engineering practitioners to easily learn, evaluate, and apply various available simulation concepts. Worked out examples are included to illustrate the concepts and an example modeling application is continued throughout the chapters to demonstrate the techniques. The book discusses modeling purposes, scoping a model, levels of modeling abstraction, the benefits and cost of including randomness, types of simulation, and statistical techniques. It also includes a chapter on modeling and simulation projects and how to conduct them for customer and engineer benefit and covers the stages of a modeling and simulation study, including process and system investigation, data collection, modeling scoping and production, model verification and validation, experimentation, and analysis of results.

Contents

Chapter 1 Introduction

1.1 Book Scope

1.2 Systems and Models

1.3 What is Simulation Modeling and Why Model?

1.4 Overview of Simulation Modeltypes

1.5 Applications

1.6 Introductory Example with Basic Measures

1.6.1 Queue-Server System

1.7 Summary

Chapter 2 Modeling and Simulation Method

2.1 Introduction

2.2 Engagement

2.3 Model Specification

2.4 Modeling

2.4.1 Modeling Paradigms

2.4.2 Model Construction

2.4.3 Data Collection and Input Analysis

2.5 Model Assessment

2.5.1 Experimentation and Output Analysis

2.6 Making Recommendations and Reporting

2.7 Knowledge and Skills for Modeling and Simulation

2.8 Summary

Chapter 3 Types of Simulation

3.1 Continuous

3.1.1 Conserved Flows and the Continuous View

3.1.2 Model Elements

3.1.3 Mathematical Formulation of System Dynamics

3.2 Discrete Event

3.2.1 Next Event Time Advance Approach

3.2.2 Common Processes and Objects in Discrete Systems

3.3 Agent Based

3.3.1 Agent-Based Model Elements

3.4 Summary

Chapter 4 Randomness

4.1 Representing Uncertain Values

4.2 Generating Random Values

4.3 Probability Distributions

4.3.1 Interpreting Probability Distributions

4.3.2 Common Probability Distributions

4.3.3 Summary and Usage of Probability Distributions

4.4 Monte Carlo Analysis

4.4.1 Inverse Transform

4.5 Summary

Chapter 5 Simulation Input Analysis

5.1 Identifying Input Variables

5.2 Deciding Types of Input Values

5.3 Collecting Data on Input Variables

5.3.1 Collecting Data

5.3.2 Eliciting Data

5.4 Estimating the Parameters of a Data Distribution

5.4.1 Using Elicited Data

5.4.2 Using Collected Data

5.4.3 Fitting Known Distributions to Collected Data

5.4.4 Constructing Empirical Distributions

5.5 Summary

Chapter 6 Simulation Runs and Output Analysis

6.1 Introduction

6.2 Preliminary Considerations

6.3 Simulation Types for Output Analysis

6.4 Model Execution

6.4.1 Setting Up Runs and Designing Experiments

6.4.2 Achieving Data Independency

6.5 Output Analysis

6.5.1 Descriptive Statistics

6.5.2 Confidence Intervals

6.5.3 Estimation of a Population Mean

6.5.4 Estimation of a Difference Between Two Means

6.5.5 Hypothesis Testing

6.5.6 Sensitivity Analysis

6.5.7 Response Surfaces

6.6 Presentation

6.7 Summary

Chapter 7 Engineering Case Study Example

7.1 Introduction and Overview

7.2 Background

7.3 Modeling

7.4 Discovering Significant Factors and Behavior

7.5 Modeling Likely Scenario and Alternatives

7.6 Findings and Impacts

7.7 Summary

Appendix A Simulation Tools

A.1 Commercial Tools

A.2 Commercial Distribution Fitting Tools

A.3 Open Source and No-Cost Tools

Appendix B Simulation Source Code Examples

B.1 Discrete Event System Models

B.1.1 Simple Model for Electric Car Charging Station

B.1.2 Full Model for Electric Car Charging Station

B.2 Continuous System Models

B.2.1 Integration Using Eulers Method

B.2.2 Integration Using the Runge-Kutta Method

Glossary

Bibliography

Index

Preface

This book presents fundamental concepts and issues in computer modeling and simulation (M&S) in a simple and practical way for engineers, scientists, and managers who wish to apply simulation successfully to their real-world problems. Enabled by computing advances, the field of M&S has become an important industrial tool for decision support, training, forecasting and planning, and marketing. For example, we want to be better informed about the challenging decisions we make about large or complex systems and processes, whether they are physical or organizational. We prefer to have evidence that supports our decisions rather than learning later that we unwittingly chose a lesser option or made a serious mistake due to lack of information. Learning how a new system will respond before building it, or what effects will be seen with changes to an existing dynamic system, can be far more cost effective than performing trials on a real system.

The applications of M&S are growing and will continue to grow. Even those who do not practice M&S are more frequently called upon to engage with modelers and simulation models. Thus, engineers and technical managers increasingly need to be conversant and knowledgeable in the concepts and techniques of M&S.

The merits of the book lie in its broadness, simplicity, and unique approach to the coverage of generic (tool-independent) M&S concepts for engineers. The book provides lessons learned from many years of working on M&S projects. This approach enables engineering practitioners to easily learn, evaluate, and apply various available simulation concepts.

Worked-out examples are included to illustrate the concepts. An example modeling application is continued throughout the chapters to demonstrate the techniques. Readers can also access the example simulation programs. Guidelines and checklists to support the modeling process are provided, and an entire chapter is devoted to an actual modeling case study that was performed in industry.

Audience

In contrast with other M&S books that are directed solely toward practitioners, this book is for other audiences including students of M&S, beginning M&S practitioners, and other stakeholders in M&S projects whether as managers, sponsors, or domain experts. Other textbooks focus primarily on the technical aspects and do not describe the broader trade-offs that a modeler faces. This book explains the larger questions and issues to be addressed.

For students of M&S, operations research, and management science, this book takes the modeler’s perspective and offers insights on the considerations that arise in an M&S project. Modeling projects are likely to fail without this broader holistic view of the M&S process.

This book also addresses beginning M&S practitioners, especially those in industry without formal training who may have been asked or seen an opportunity to produce a dynamic model. Simulation software tools have advanced dramatically in recent decades with graphical interfaces, specialized libraries, and open-source software that allow easy entry into the practice of M&S. However, those who attempt to use these software packages without adequate preparation may find that that their models provide little, if any, benefit. They may lack a well-rounded understanding of M&S as a statistical tool for policy analysis and decision support. Six Sigma and other quality practitioners are in this category, even th...

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