The second case (Section A.2) analyzes an amusement park and considers a “Fast-Pass” ticket option and the impact it might have on waiting times. The first case (Section A.1) describes a manufacturing system with machining, inspection, and cleaning operations, and involves analyzing the current system configuration along with two proposed improvements. However, unlike the models in previous chapters, we do not provide detailed descriptions of the models themselves. For the first two cases, we’ve also provided some analysis results based on our sample models. These problems are larger in scope and are not as well-defined as the Problems in previous chapters. This chapter includes four “introductory” and two “advanced” case studies involving the development and use of Simio models to analyze systems. C.4.5 Solutions and Other Files For Instructors.B.6 Pulp and Paper Manufacturing Supply.12.13.1 Simio E-books on Planning and Scheduling.12.13 Additional Information and Examples.12.12.3 Running and Analyzing the Model.12.12.1 Configuring the Model for Data Import.12.12 Model 12-2: Data First Approach to Scheduling.12.11.3 Adding Performance Tracking and Targets.12.11.1 Building a Simple Scheduling Model.12.11 Model 12-1: Model First Approach to Scheduling.12.9 Planning and Scheduling With Simio RPS.12.8 Risk-based Planning and Scheduling.12.6 Tough Problems in Planning and Scheduling.12.5 The Role of Simulation-based Scheduling.12.4 Role of Design Simulation in Industry 4.0.12.2 The Fourth Industrial Revolution – The Smart Factory.12.1.3 Third Industrial Revolution – The Digital Age.12.1.2 Second Industrial Revolution – Mass Production.12.1.1 First Industrial Revolution – Mechanical Production.12.1 Industrial Revolutions through the Ages.12 Simulation-based Scheduling in Industry 4.0.11.5.1 How to Create and Deploy a User Extension.11.4 Model 11-3: Sub-Classing an Object.11.3 Model 11-2: Building a Base Object.11.2 Model 11-1: Building a Hierarchical Object.11.1.3 Sub-classing an Object Definition.11.1 Basic Concepts of Defining Objects.10.1.2 Model 10-2: Accumulating a Total Process Time in a Batch.10.1.1 Model 10-1: Searching For and Removing Entities from a Station.9.1.2 Ranking and Selection of Alternate Scenarios in Model 9-1 With Subset Selection and KN.9.1.1 Seeking Optimal Resource Levels in Model 9-1 With OptQuest.8.2.7 Model 8-3: ED Enhanced with Hospital Staff.8.2.4 Model 8-2: PCB Assembly with Conveyors.8.2.2 Using Connectors, TimePaths, and Paths.8.2.1 Entity Movement Through Free Space. 8.1.7 Model 8-1: Animating the PCB Assembly.8.1.5 Editing Symbols with the Symbols Ribbon.8.1.4 Status Animation With the Animation Ribbon.8.1.3 Background Animation With the Drawing Ribbon.7.1.4 Model 7-2: Enhanced ED Using Sequence Tables.7.1.2 Model 7-1: An ED Using a Data Table.6.4 Generating Random Variates and Processes.6.2.2 Scalar vs. Multivariate vs. Stochastic Processes.6.1.5 More on Assessing Goodness of Fit.6.1.4 Fitting Distributions to Observed Real-World Data.6.1.3 Choosing Probability Distributions.6.1.2 Options for Using Observed Real-World Data.6.1 Specifying Univariate Input Probability Distributions.5.5 Model 5-4: Comparing Multiple Alternative Scenarios.5.4 Model 5-3: PCB Model With Process Selection.5.3.2 Using Expressions with Link Weights.4.7 Interactive Logs and Dashboard Reports.4.6 Exporting Output Data for Further Analysis.4.5 Beyond Means: Simio MORE (SMORE) Plots.4.4 Model 4-3: Automated Teller Machine (ATM).4.3 Model 4-2: First Model Using Processes.4.2.4 Steady-State vs. Terminating Simulations.4.2.3 Replications and Statistical Analysis of Output.4.2.2 Initial Experimentation and Analysis.4.2 Model 4-1: First Project Using the Standard Library Objects.4.1.8 Moving/Configuring Windows and Tabs.3.4.2 Special-Purpose Simulation Software.3.4.1 General-Purpose Programming Languages.3.4 Software Options for Dynamic Simulation.3.3.2 Model 3-6: Single-Server Queueing Delays.3.3.1 Model 3-5: Manual Dynamic Simulation.3.3 Dynamic Simulation Without Special Software.3.2.4 Model 3-4: New Product Decision Model.3.2.3 Model 3-3: Single-Period Inventory Profits.3.2.2 Model 3-2: Monte-Carlo Integration.3.1.3 Deterministic vs. Stochastic Models.3.1.2 Continuous-Change vs. Discrete-Change Dynamic Models.2.3 Specific Results for Some Multiserver Queueing Stations.2.1 Queueing-System Structure and Terminology.1.5.5 Stakeholder and Simulationist Bills of Rights.1.3.1 Randomness in Simulation and Random Variables.1.3 Randomness and the Simulation Process.
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