| ACKNOWLEDGEMENTS. |
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| PREFACE. |
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CHAPTER 1: www.simulation: What, Why and When? |
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1.3.1 The nature of operations systems: variability, interconnectedness and complexity. |
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1.3.2 The advantages of simulation. |
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1.3.3 The disadvantages of simulation. |
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CHAPTER 2: Inside Simulation Software. |
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2.2 Modelling the progress of time. |
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2.2.1 The time-slicing approach. |
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2.2.2 The discrete-event simulation approach (three-phase method). |
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2.2.3 The continuous simulation approach. |
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2.2.4 Summary: modelling the progress of time. |
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2.3 Modelling variability. |
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2.3.1 Modelling unpredictable variability. |
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2.3.3 Relating random numbers to variability in a simulation. |
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2.3.4 Modelling variability in times. |
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2.3.5 Sampling from standard statistical distributions. |
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2.3.6 Computer generated random numbers. |
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2.3.7 Modelling predictable variability. |
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2.3.8 Summary on modelling variability. |
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CHAPTER 3: Software for Simulation. |
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3.2 Visual interactive simulation. |
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3.3.2 Programming languages. |
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3.3.3 Specialist simulation software. |
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3.3.4 Comparing spreadsheets, programming languages and specialist simulation software. |
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3.4 Selection of simulation software. |
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3.4.1 The process of software selection. |
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3.4.2 Step 1: Establish the modelling requirements. |
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3.4.3 Step 2: Survey and shortlist the software. |
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3.4.4 Step 3: Establish evaluation criteria. |
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3.4.5 Step 4: Evaluate the software in relation to the criteria. |
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3.4.6 Step 5: Software selection. |
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CHAPTER 4: Simulation Studies: An Overview. |
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4.2 Simulation studies: an overview of key modelling processes. |
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4.2.1 Simulation modelling is not linear. |
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4.2.2 Something is missing! |
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4.3 Simulation project time-scales. |
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4.4 The simulation project team. |
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4.5 Hardware and software requirements. |
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CHAPTER 5: Conceptual Modelling. |
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5.2 Conceptual modelling: important but little understood. |
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5.3 What is a conceptual model? |
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5.4 Requirements of the conceptual model. |
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5.4.1 Four requirements of a conceptual model. |
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5.4.2 Keep the model simple. |
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5.5 Communicating the conceptual model. |
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5.5.1 Simulation project specification. |
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5.5.2 Representing the conceptual model. |
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CHAPTER 6: Developing the Conceptual Model. |
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6.2 A Framework for conceptual modelling. |
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6.2.1 Developing an understanding of the problem situation. |
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6.2.2 Determining the modelling objectives. |
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6.2.3 Designing the conceptual model: the inputs and outputs. |
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6.2.4 Designing the conceptual model: the model content. |
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6.2.5 The role of data in conceptual modelling. |
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6.2.6 Summary of the conceptual modelling framework. |
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6.3 Methods of model simplification. |
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6.3.1 Aggregation of model components. |
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6.3.2 Excluding components and details. |
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6.3.3 Replacing components with random variables. |
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6.3.4 Excluding infrequent events. |
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6.3.5 Reducing the rule set. |
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6.3.7 What is a good simplification? |
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CHAPTER 7: Data Collection and Analysis. |
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7.3.1 Dealing with unobtainable (category C) data. |
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7.4 Representing unpredictable variability. |
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7.4.2 Empirical distributions. |
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7.4.3 Statistical distributions. |
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7.4.4 Traces versus empirical distributions versus statistical distributions. |
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7.4.6 Further issues in representing unpredictable variability: correlation and non-stationary data. |
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7.5 Selecting statistical distributions. |
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7.5.1 Selecting distributions from known properties of the process. |
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7.5.2 Fitting statistical distributions to empirical data. |
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8.2 Structuring the model. |
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8.3.1 Separate the data from the code from the results. |
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8.3.2 Use of pseudo random number streams. |
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8.4 Documenting the model and the simulation project. |
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CHAPTER 9: Experimentation: Obtaining Accurate Results. |
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9.2 The nature of simulation models and simulation output. |
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9.2.1 Terminating and non-terminating simulations. |
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9.2.3 Steady-state output. |
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9.2.4 Other types of output. |
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9.2.5 Determining the nature of the simulation output. |
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9.3 Issues in obtaining accurate simulation results. |
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9.3.1 Initialization bias: warm-up and initial conditions. |
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9.3.2 Obtaining sufficient output data: long runs and multiple replications. |
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9.4 An example model: computer user help desk. |
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9.5 Dealing with initialization bias: warm-up and initial conditions. |
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9.5.1 Determining the warm-up period. |
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9.5.2 Setting initial conditions. |
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9.5.3 Mixed initial conditions and warm-up. |
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9.5.4 Initial conditions versus warm-up. |
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9.6 Selecting the number of replications and run-length. |
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9.6.1 Performing multiple replications. |
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9.6.2 Variance reduction (antithetic variates). |
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9.6.3 Performing a single long run. |
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9.6.4 Multiple replications versus long runs. |
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CHAPTER 10: Experimentation: Searching the Solution Space. |
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10.2 The nature of simulation experimentation. |
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10.2.1 Interactive and batch experimentation. |
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10.2.2 Comparing alternatives and search experimentation. |
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10.3 Analysis of results from a single scenario. |
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10.3.2 Measures of variability. |
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10.4 Comparing alternatives. |
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10.4.1 Comparison of two scenarios. |
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10.4.2 Comparison of many scenarios. |
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10.4.3 Choosing the best scenario(s). |
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10.5 Search experimentation. |
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10.5.1 Informal approaches to search experimentation. |
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10.5.2 Experimental design. |
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10.5.4 Optimization ("searchization"). |
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10.6 Sensitivity analysis. |
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CHAPTER 11: Implementation. |
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11.2 What is implementation? |
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11.2.1 Implementing the findings. |
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11.2.2 Implementing the model. |
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11.2.3 Implementation as learning. |
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11.3 Implementation and simulation project success. |
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11.3.1 What is simulation project success? |
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11.3.2 How is success achieved? |
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11.3.3 How is success measured? |
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CHAPTER 12: Verification, Validation and Confidence. |
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12.2 What is verification and validation? |
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12.3 The difficulties of verification and validationCHAPTER 13: The Practice of Simulation. |
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13.2 Types of simulation model. |
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13.3 Modes of simulation practice. |
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13.3.1 Three modes of practice. |
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13.3.2 Facets of the modes of simulation practice. |
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13.3.3 Modes of practice in business and the military. |
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APPENDIX 1: Wardeon Cinema. |
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APPENDIX 2: Panorama Televisions. |
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APPENDIX 3: Methods of reporting simulation results. |
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APPENDIX 4: Statistical distributions. |
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APPENDIX 5: Critical values for the chi-square test. |
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APPENDIX 6: Critical values for the Student’s t-distribution. |
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