Simulation The Practice of Model Development and Use 1st Edition by Stewart Robinson ISBN 978-0470847725 0470847727
Original price was: $50.00.$35.00Current price is: $35.00.
Simulation The Practice of Model Development and Use 1st Edition by Stewart Robinson – Ebook PDF Instant Download/Delivery: 978-0470847725, 0470847727
Full download Simulation The Practice of Model Development and Use 1st Edition after payment
Product details:
ISBN 10: 0470847727
ISBN 13: 978-0470847725
Author: Stewart Robinson
Simulation modelling involves the development of models that imitate real-world operations, and statistical analysis of their performance with a view to improving efficiency and effectiveness. This non-technical textbook is focused towards the needs of business, engineering and computer science students, and concentrates on discrete event simulations as it is used in operations management. Stewart Robinson of Warwick Business School offers guidance through the key stages in a simulation project in terms of both the technical requirements and the project management issues surrounding it. Readers will emerge able to develop appropriate valid conceptual models, perform simulation experiments, analyse the results and draw insightful conclusions.
Table of contents:
CHAPTER 1
www.simulation: What, Why and When?
1.1 Introduction
1.2 What is simulation?
1.3 Why simulate?
1.3.1 The nature of operations systems: variability, interconnectedness and complexity
1.3.2 The advantages of simulation
1.3.3 The disadvantages of simulation
1.4 When to simulate
1.5 Conclusion
Exercises
References
CHAPTER 2
Inside Simulation Software
2.1 Introduction
2.2 Modelling the progress of time
2.2.1 The time-slicing approach
2.2.2 The discrete-event simulation approach
(three-phase method)
2.2.3 The continuous simulation approach.
2.2.4 Summary: modelling the progress of time
2.3 Modelling variability
2.3.1 Modelling unpredictable variability.
2.3.2 Random numbers
2.3.3 Relating random numbers to variability in a simulation
2.3.4 Modelling variability in times
2.3.5 Sampling from standard statistical distributions
2.3.6 Computer generated random numbers
2.3.7 Modelling predictable variability
2.3.8 Summary on modelling variability
2.4 Conclusion
Exercises
References.
CHAPTER 3
Software for Simulation
3.1 Introduction
3.2 Visual interactive simulation
3.3 Simulation software
3.3.1 Spreadsheets
3.3.2 Programming languages
3.3.3 Specialist simulation software
3.3.4 Comparing spreadsheets, programming languages and specialist simulation software
3.4 Selection of simulation software
3.4.1 The process of software selection
3.4.2 Step 1: Establish the modelling requirements
3.4.3 Step 2: Survey and shortlist the software
3.4.4 Step 3: Establish evaluation criteria
3.4.5 Step 4: Evaluate the software in relation to the criteria
3.4.6 Step 5: Software selection.
3.5 Conclusion
References
CHAPTER 4
Simulation Studies: An Overview
4.1 Introduction
4.2 Simulation studies: an overview of key modelling processes
4.2.1 Simulation modelling is not linear
4.2.2 Something is missing!
4.3 Simulation project time-scales
4.4 The simulation project team
4.5 Hardware and software requirements
4.6 Project costs.
4.7 Project selection
4.8 Conclusion
References.
CHAPTER 5
Conceptual Modelling
5.1 Introduction
5.2 Conceptual modelling: important but little understood
5.3 What is a conceptual model
?
5.4 Requirements of the conceptual model
5.4.1 Four requirements of a conceptual model
5.4.2 Keep the model simple.
5.5 Communicating the conceptual model.
5.5.1 Simulation project specification
5.5.2 Representing the conceptual model
5.6 Conclusion
Exercise
References
CHAPTER 6
Developing the Conceptual Model
6.1 Introduction
6.2 A Framework for conceptual modelling
6.2.1 Developing an understanding of the problem situation
6.2.2 Determining the modelling objectives
6.2.3 Designing the conceptual model: the inputs and outputs
6.2.4 Designing the conceptual model: the model content.
6.2.5 The role of data in conceptual modelling
6.2.6 Summary of the conceptual modelling framework
6.3 Methods of model simplification.
6.3.1 Aggregation of model components
6.3.2 Excluding components and details
6.3.3 Replacing components with random variables
6.3.4 Excluding infrequent events
6.3.5 Reducing the rule set
6.3.6 Splitting models
6.3.7 What is a good simplification?
6.4 Conclusion
Exercises
References
CHAPTER 7
Data Collection and Analysis
7.1 Introduction
7.2 Data requirements.
7.3 Obtaining data
7.3.1 Dealing with unobtainable (category C) data
7.3.2 Data accuracy
7.3.3 Data format
7.4 Representing unpredictable variability
7.4.1 Traces
7.4.2 Empirical distributions
7.4.3 Statistical distributions
7.4.4 Traces versus empirical distributions versus statistical distributions
7.4.5 Bootstrapping
7.4.6 Further issues in representing unpredictable variability: correlation and non-stationary data
7.5 Selecting statistical distributions
7.5.1 Selecting distributions from known properties of the process
7.5.2 Fitting statistical distributions to empirical data.
7.6 Conclusion
Exercises
References .
CHAPTER 8
Model Coding
8.1 Introduction
8.2 Structuring the model
8.3 Coding the model
8.3.1 Separate the data from the code from the results
8.3.2 Use of pseudo random number streams
8.4 Documenting the model and the simulation project
8.5 Conclusion
Exercises
References
CHAPTER 9
Experimentation: Obtaining Accurate Results
9.1 Introduction
9.2 The nature of simulation models and simulation output
9.2.1 Terminating and non-terminating simulations
9.2.2 Transient output.
9.2.3 Steady-state output
9.2.4 Other types of output.
9.2.5 Determining the nature of the simulation output
9.3 Issues in obtaining accurate simulation results
9.3.1 Initialization bias: warm-up and initial conditions
9.3.2 Obtaining sufficient output data: long runs and multiple replications
9.4 An example model: computer user help desk.
9.5 Dealing with initialization bias: warm-up and initial conditions
9.5.1 Determining the warm-up period
9.5.2 Setting initial conditions
9.5.3 Mixed initial conditions and warm-up
9.5.4 Initial conditions versus warm-up
9.6 Selecting the number of replications and run-length.
9.6.1 Performing multiple replications
9.6.2 Variance reduction (antithetic variates).
9.6.3 Performing a single long run
9.6.4 Multiple replications versus long runs
9.7 Conclusion
Exercises
References
CHAPTER 10
Experimentation: Searching the Solution Space
10.1 Introduction
10.2 The nature of simulation experimentation
10.2.1 Interactive and batch experimentation
10.2.2 Comparing alternatives and search experimentation
10.3 Analysis of results from a single scenario
10.3.1 Point estimates
10.3.2 Measures of variability
10.4 Comparing alternatives
10.4.1 Comparison of two scenarios
10.4.2 Comparison of many scenarios
10.4.3 Choosing the best scenario(s)
10.5 Search experimentation
10.5.1 Informal approaches to search experimentation
10.5.2 Experimental design
10.5.3 Metamodelling
10.5.4 Optimization (“searchization”)
10.6 Sensitivity analysis
10.7 Conclusion
Exercises.
References
CHAPTER 11
Implementation
11.1 Introduction
11.2 What is implementation?
11.2.1 Implementing the findings
11.2.2 Implementing the model 11.2.3 Implementation as learning
11.3 Implementation and simulation project success
11.3.1 What is simulation project success?.
11.3.2 How is success achieved?
11.3.3 How is success measured?
11.4 Conclusion
References.
CHAPTER 12
Verification, Validation and Confidence
12.1 Introduction
12.2 What is verification and validation?
12.3 The difficulties of verification and validation.
12.3.1 There is no such thing as general validity
12.3.2 There may be no real world to compare against
12.3.3 Which real world?
12.3.4 Often the real world data are inaccurate
12.3.5 There is not enough time to verify and validate everything
12.3.6 Confidence not validity
12.4 Methods of verification and validation
12.4.1 Conceptual model validation.
12.4.2 Data validation
12.4.3 Verification and white-box validation
12.4.4 Black-box validation
12.4.5 Experimentation validation.
12.4.6 Solution validation
12.5 Independent verification and validation
12.6 Conclusion
Exercises
References.
CHAPTER 13
The Practice of Simulation
13.1 Introduction
13.2 Types of simulation model
13.3 Modes of simulation practice
13.3.1 Three modes of practice
13.3.2 Facets of the modes of simulation practice
13.3.3 Modes of practice in business and the military
13.4 Conclusion
References.
People also search for:
simulation the practice of model development and use palgrave 2014
simulation the practice of model development and use stewart robinson
what is simulation theory
the practice of model development and use
simulation development
Tags: Stewart Robinson, Simulation The Practice, Development and Use