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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Teaches basic and advanced modeling and simulation techniques to both undergraduate and postgraduate students and serves as a practical guide and manual for professionals learning how to build simulation models using WITNESS, a free-standing software package.
This book discusses the theory behind simulation and demonstrates how to build simulation models with WITNESS. The book begins with an explanation of the concepts of simulation modeling and a “guided tour” of the WITNESS modeling environment. Next, the authors cover the basics of building simulation models using WITNESS and modeling of material-handling systems. After taking a brief tour in basic probability and statistics, simulation model input analysis is then examined in detail, including the importance and techniques of fitting closed-form distributions to observed data. Next, the authors present simulation output analysis including determining run controls and statistical analysis of simulation outputs and show how to use these techniques and others to undertake simulation model verification and validation. Effective techniques for managing a simulation project are analyzed, and case studies exemplifying the use of simulation in manufacturing and services are covered. Simulation-based optimization methods and the use of simulation to build and enhance lean systems are then discussed. Finally, the authors examine the interrelationships and synergy between simulation and Six Sigma.
- Emphasizes real-world applications of simulation modeling in both services and manufacturing sectors
- Discusses the role of simulation in Six Sigma projects and Lean Systems
- Contains examples in each chapter on the methods and concepts presented
Process Simulation Using WITNESS is a resource for students, researchers, engineers, management consultants, and simulation trainers.
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Seitenzahl: 860
Veröffentlichungsjahr: 2015
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Table of Contents
COVER
TITLE PAGE
COPYRIGHT
DEDICATION
ABOUT THE COMPANION WEBSITE
PREFACE
ACKNOWLEDGMENTS
CHAPTER 1: CONCEPTS OF SIMULATION MODELING
1.1 OVERVIEW
1.2 SYSTEM MODELING
1.3 SIMULATION MODELING
1.4 THE ROLE OF SIMULATION
1.5 SIMULATION METHODOLOGY
1.6 STEPS IN A SIMULATION STUDY
1.7 SIMULATION SOFTWARE
1.8 SUMMARY
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 2: WORLD-VIEWS OF SIMULATION
2.1 OVERVIEW
2.2 SYSTEM MODELING WITH DES
2.3 ELEMENTS OF DISCRETE EVENT SIMULATION (DES)
2.4 DES FUNCTIONALITY
2.5 EXAMPLE OF DES MECHANISMS
2.6 MONTE CARLO SIMULATION (MCS)
2.7 CONTINUOUS SIMULATION
2.8 WITNESS
®
WORLD-VIEWS OF SIMULATION
2.9 SUMMARY
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 3: WITNESS
®
ENVIRONMENT
3.1 OVERVIEW
3.2 THE WITNESS
®
ENVIRONMENT
3.3 MENUS
3.4 TOOL BARS
3.5 DIALOG BOXES AND PROPERTY SHEETS
3.6 WINDOWS
3.7 LAYERS
3.8 THE WITNESS
®
EDITOR
3.9 WINDOW OPERATIONS
3.10 THE HELP FACILITY
3.11 THE BASIC ELEMENTS
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 4: BASIC WITNESS
®
MODELING TECHNIQUES
4.1 OVERVIEW
4.2 STEP-BY-STEP MODEL BUILDING
4.3 MODELING A SIMPLE MANUFACTURING PROCESS
4.4 MODELING A SERVICE PROCESS
4.5 WITNESS
®
CODE
4.6 AN EXTENDED EXAMPLE
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 5: MODELING MATERIAL HANDLING SYSTEMS
5.1 OVERVIEW
5.2 MATERIAL HANDLING SYSTEMS
5.3 MATERIAL HANDLING SYSTEMS IN WITNESS
®
5.4 MODELING CONVEYORS
5.5 MODELING PATHS FOR LABOR AND PARTS TRANSIT
5.6 MODELING VEHICLES AND TRACKS
5.7 MODELING POWER-&-FREE SYSTEMS
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 6: BASIC PROBABILITY AND STATISTICS FOR SIMULATION
6.1 OVERVIEW
6.2 RANDOM VARIABLES (RV)
6.3 POINT ESTIMATION
6.4 CONFIDENCE INTERVALS FOR THE POPULATION MEAN
6.5 CONFIDENCE INTERVALS FOR THE POPULATION VARIANCE AND STANDARD DEVIATION
6.6 SAMPLE SIZE DETERMINATION WHEN ESTIMATING POPULATION MEAN
6.7 THEORETICAL PROBABILITY DISTRIBUTIONS
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 7: SIMULATION INPUT MODELING
7.1 OVERVIEW
7.2 DETERMINING DATA REQUIREMENTS
7.3 METHODS OF DATA COLLECTION
7.4 REPRESENTING COLLECTED DATA
7.5 VALIDATING COLLECTED DATA
7.6 FITTING PROBABILITY DISTRIBUTIONS TO COLLECTED DATA
7.7 WITNESS
®
INPUT MODELING
7.8 PRACTICAL ASPECTS OF INPUT MODELING
7.9 SUMMARY
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 8: SIMULATION OUTPUT ANALYSIS
8.1 OVERVIEW
8.2 TERMINATING VERSUS STEADY-STATE SIMULATION
8.3 DETERMINING SIMULATION RUN CONTROLS
8.4 VARIABILITY IN SIMULATION OUTPUTS
8.5 SIMULATION OUTPUT ANALYSIS
8.6 EXAMPLE: OUTPUT ANALYSES OF A CLINIC SIMULATION
8.7 WITNESS
®
MODULES FOR SIMULATION OUTPUT ANALYSIS
8.8 SUMMARY
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 9: MODEL VERIFICATION AND VALIDATION TECHNIQUES
9.1 OVERVIEW
9.2 MODEL VERIFICATION TECHNIQUES
9.3 MODEL VALIDATION TECHNIQUES
9.4 VERIFYING WITNESS
®
MODELS
9.5 SUMMARY
QUESTION AND EXERCISE
BIBLIOGRAPHY
CHAPTER 10: SIMULATION PROJECT MANAGEMENT
10.1 OVERVIEW
10.2 DEFINE THE PROBLEM
10.3 DESIGN THE STUDY
10.4 DESIGN THE CONCEPTUAL MODEL
10.5 FORMULATE INPUTS, ASSUMPTIONS, AND PROCESS DEFINITION
10.6 BUILD, VERIFY, AND VALIDATE THE MODEL
10.7 EXPERIMENT WITH THE MODEL
10.8 DOCUMENTATION AND PRESENTATION
10.9 DEFINE THE MODEL LIFE CYCLE
10.10 SUMMARY
BIBLIOGRAPHY
CHAPTER 11: MANUFACTURING SIMULATION CASE STUDIES
11.1 OVERVIEW
11.2 HYBRID SIMULATION OF TITANIUM MANUFACTURING PROCESS
11.3 PAINT CAPACITY STUDY OF AN AVIATION COMPANY
11.4 SIMULATION OF A SEAMLESS PIPE FACILITY
11.5 SUMMARY
BIBLIOGRAPHY
CHAPTER 12: SERVICE SIMULATION CASE STUDIES
12.1 OVERVIEW
12.2 ELEMENTS OF SERVICE SYSTEMS
12.3 CHARACTERISTICS OF SERVICE SYSTEMS
12.4 MODELING SERVICE SYSTEMS
12.5 APPLICATIONS OF SERVICE SYSTEM SIMULATION
12.6 CASE STUDIES ON SERVICE SYSTEMS SIMULATION
12.7 SUMMARY
BIBLIOGRAPHY
CHAPTER 13: SIMULATION-BASED OPTIMIZATION METHODS
13.1 OVERVIEW
13.2 OPTIMIZATION APPROACHES IN SIMULATION STUDIES
13.3 SIMULATION-BASED OPTIMIZATION
13.4 WITNESS
®
EXPERIMENTER
13.5 OPTIMIZATION WITHIN THE WITNESS
®
EXPERIMENTER
13.6 SUMMARY
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
CHAPTER 14: SIMULATION FOR LEAN SYSTEMS
14.1 OVERVIEW
14.2 BASICS OF LEAN SYSTEMS
14.3 SIMULATION-BASED LEAN SYSTEMS
14.4 LEAN USING WITNESS
®
14.5 SUMMARY
QUESTION AND EXERCISES
BIBLIOGRAPHY
CHAPTER 15: SIMULATION FOR SIX SIGMA
15.1 OVERVIEW
15.2 SIX SIGMA QUALITY
15.3 SIX SIGMA METHODS
15.4 WITNESS
®
FOR SIX SIGMA
15.5 SIMULATION-BASED SIX SIGMA
15.6 SUMMARY
QUESTIONS AND EXERCISES
BIBLIOGRAPHY
APPENDIX
INDEX
End User License Agreement
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Guide
Cover
Table of Contents
Preface
Begin Reading
List of Illustrations
CHAPTER 1: CONCEPTS OF SIMULATION MODELING
Figure 1.1 Definition of system concept.
Figure 1.2 The process of system modeling.
Figure 1.3 Types of system models.
Figure 1.4 Examples of product prototypes.
Figure 1.5 Example of a process graphical model.
Figure 1.6 An example of a MATLAB mathematical model.
Figure 1.7 Example of a FEA computer model.
Figure 1.8 Example of a DES model built-in WITNESS
®
software.
Figure 1.9 The simulation process.
Figure 1.10 A deterministic model with stochastic inputs and response.
Figure 1.11 Static and dynamic model variables.
Figure 1.12 Simulation taxonomy.
Figure 1.13 Systematic simulation approach.
Figure 1.14 The simulation procedure.
Figure 1.15 Developing a model concept.
Figure 1.16 Collecting model data.
Figure 1.17 Model building procedure.
Figure 1.18 Continuous improvement with simulation.
Figure 1.19 Documenting a simulation study.
Figure 1.20 Elements of a simulation report.
CHAPTER 2: WORLD-VIEWS OF SIMULATION
Figure 2.1 Elements of system modeling with DES.
Figure 2.2 DES system elements.
Figure 2.3 System state variables.
Figure 2.4 Events-driven system state.
Figure 2.5 Activities in process operations chart.
Figure 2.6 DES functionality.
Figure 2.7 EL operations.
Figure 2.8 Time advancement and compression in DES.
Figure 2.9 Example of 3D WITNESS
®
simulation animation.
Figure 2.10 Execution of a car arrival event.
Figure 2.11 Randomness propagation with MCS.
Figure 2.12 The histogram of the generated 500 iterations.
Figure 2.13 The histogram of the generated 5000 iterations.
Figure 2.14 WITNESS
®
modeling elements.
CHAPTER 3: WITNESS
®
ENVIRONMENT
Figure 3.1 File START.MOD.
Figure 3.2 File drop-down menu.
Figure 3.3 Standard tool bar.
Figure 3.4 Views tool bar.
Figure 3.5 Element tool bar.
Figure 3.6 Model toolbar.
Figure 3.7 Assistant toolbar.
Figure 3.8 Run tool bar.
Figure 3.9 Reporting toolbar.
Figure 3.10 Display edit tool bar.
Figure 3.11 Customize tool bar.
Figure 3.12 Activating, deactivating toolbars.
Figure 3.13 A WITNESS
®
dialog box.
Figure 3.14 A sample WITNESS
®
editor.
Figure 3.15 Window control.
Figure 3.16 Interact box.
Figure 3.17 Digital time.
Figure 3.18 Analog clock.
Figure 3.19 WITNESS
®
help system.
CHAPTER 4: BASIC WITNESS
®
MODELING TECHNIQUES
Figure 4.1 Flow diagram of first WITNESS
®
example.
Figure 4.2 Detail part window.
Figure 4.3 Machine detail window.
Figure 4.4 Input rule editor window.
Figure 4.5 Output rule window.
Figure 4.6 Detail buffer window.
Figure 4.7 WITNESS
®
layout of first example.
Figure 4.8 Display toolbar.
Figure 4.9 Define clock window.
Figure 4.10 Output report for polisher.
Figure 4.11 Output report for HoldArea.
Figure 4.12 Detail variable window.
Figure 4.13 Edit actions on output window.
Figure 4.14 Model elements display.
Figure 4.15 Detail labor window.
Figure 4.16 Breakdowns definition window.
Figure 4.17 Breakdown definition window after adding a breakdown.
Figure 4.18 Labor statistics report.
Figure 4.19 Flow of tourists visiting museum.
Figure 4.20 Detail part window for active arrivals.
Figure 4.21 Detail distribution window.
Figure 4.22 Window for definition of setups.
Figure 4.23 Detail shift window.
Figure 4.24 Assigning a shift to a resource and specifying allowance.
Figure 4.25 Selection of elements for results reporting.
Figure 4.26 WITNESS
®
service model example.
Figure 4.27 Results reporting on selected elements.
Figure 4.28 “Where Used” report for “Show_German” machine.
Figure 4.29 Creating and naming a histogram.
Figure 4.30 Logging a value into a histogram.
Figure 4.31 Histogram of wait times of German-speaking tourists.
Figure 4.32 Histogram statistical report.
Figure 4.33 Screenshot of harbor example.
Figure 4.34 Representation of the ship loading time via “Option: Min.”
Figure 4.35 “Display Histogram” window permits detailed modification.
CHAPTER 5: MODELING MATERIAL HANDLING SYSTEMS
Figure 5.1 Specification of work search.
Figure 5.2 Defining the first cycle of a multi-cycle machine.
Figure 5.3 Two cycles added for a multicycle machine.
Figure 5.4 Machine multicycle grid.
Figure 5.5 Detail conveyor window.
Figure 5.6 Conveyor downtime specification.
Figure 5.7 Typical conveyor statistics report.
Figure 5.8 Animation of indexed fixed conveyor.
Figure 5.9 Detail path window.
Figure 5.10 Path operation detail in the model options window.
Figure 5.11 Paths statistics summary report.
Figure 5.12 Rule to request Part movement by Labor.
Figure 5.13 Specifying details of preemption logic.
Figure 5.14 Insertion of a track between two machines.
Figure 5.15 Detail track window.
Figure 5.16 Specification of unloading at front of downstream track.
Figure 5.17 Specification of loading at front of upstream track.
Figure 5.18 Incorrect attempt to run model without vehicle.
Figure 5.19 Detail vehicle dialog window.
Figure 5.20 WITNESS
®
report for the downstream and upstream tracks.
Figure 5.21 Output results for the vehicle “Cart.”
Figure 5.22 Flow diagram for introductory power-&-free (P&F) example.
Figure 5.23 Definition of a Power & Free Network.
Figure 5.24 Definition of P&F carriers.
Figure 5.25 Definition of P&F sections.
Figure 5.26 Definition of loading station – general.
Figure 5.27 Definition of loading station – load logic.
Figure 5.28 Definition of action station – general.
Figure 5.29 Definition of action station – actions logic.
Figure 5.30 Definition of unloading station – general.
Figure 5.31 Definition of unloading station – unload logic.
Figure 5.32 Report on Power-&-Free Network Sections.
Figure 5.33 Report on Power-&-Free Network Sections.
CHAPTER 6: BASIC PROBABILITY AND STATISTICS FOR SIMULATION
Figure 6.1 Point estimators for mean and standard deviation.
Figure 6.2 Calculation of confidence intervals.
Figure 6.3 Typical histogram of values generated from uniform distribution.
Figure 6.4 Theoretical density function of the same uniform distribution.
Figure 6.5 Typical histogram of values generated from normal distribution.
Figure 6.6 Theoretical density function of the same normal distribution.
Figure 6.7 Typical histogram of values generated from exponential distribution.
Figure 6.8 Theoretical density function of the same exponential distribution.
Figure 6.9 Typical histogram of values generated from Erlang distribution.
Figure 6.10 Theoretical density function of the same Erlang distribution.
Figure 6.11 Typical histogram of values generated from gamma distribution.
Figure 6.12 Theoretical density function of the same gamma distribution.
Figure 6.13 Typical histogram of values generated from Weibull distribution.
Figure 6.14 Theoretical density function of the same Weibull distribution.
Figure 6.15 Typical histogram of values generated from a triangular distribution.
Figure 6.16 Theoretical density function of the same triangular distribution.
CHAPTER 7: SIMULATION INPUT MODELING
Figure 7.1 A simple time study example.
Figure 7.2 Data collected using an operation chart.
Figure 7.3 Process flow of widget manufacturing process.
Figure 7.4 Data boxes for collecting process data.
Figure 7.5 Example of sample data and their summary statistics.
Figure 7.6 A Histogram of cycle times.
Figure 7.7 A graphical and statistical summary of collected data.
Figure 7.8 The Histogram of the filtered cycle times.
Figure 7.9 The summary statistics for filtered data.
Figure 7.10 Scatter plot check of data independence.
Figure 7.11 Autocorrelation test of data independence.
Figure 7.12 Median run test for data independence.
Figure 7.13 Turning points of data independence.
Figure 7.14 Results of Chi-Square goodness-of-fit test.
Figure 7.15 The Chi-Square test resulting graph (fitted density).
Figure 7.16 A graph of density function.
Figure 7.17 WITNESS
®
random number control dialog box.
Figure 7.18 Vehicles flow in the service center.
Figure 7.19 Histogram of inter-arrival times.
Figure 7.20 Scatter plot of inter-arrival times.
Figure 7.21 Histogram of service times.
Figure 7.22 Scatter plot of service times.
Figure 7.23 A conceptual model of the ER example.
CHAPTER 8: SIMULATION OUTPUT ANALYSIS
Figure 8.1 Example of a bank terminating simulation.
Figure 8.2 Example of a plant nonterminating simulation
.
Figure 8.3 The graphical determination of warm-up period.
Figure 8.4 Method of batch means
.
Figure 8.5 Method of independent multiple replications.
Figure 8.6 Sources of variation in a simulation model.
Figure 8.7 Hourly throughput for a 100-hour run time.
Figure 8.8 Histogram and normal curve of throughput data.
Figure 8.9 Box plot of throughput data.
Figure 8.10 Dot plot of throughput data.
Figure 8.11 Statistical and graphical summary with confidence intervals.
Figure 8.12 Parameter design with simulation.
Figure 8.13 MINITAB experimental design.
Figure 8.14 MINITAB main effects plot.
Figure 8.15 MINITAB interaction plots.
Figure 8.16 A Process map of patients flow in the clinic.
Figure 8.17 Examples of WITNESS
®
output (report on machine statistics).
Figure 8.18 Examples of WITNESS
®
outputs (machine time-in-state chart).
CHAPTER 9: MODEL VERIFICATION AND VALIDATION TECHNIQUES
Figure 9.1 Model verification and validation process.
Figure 9.2 Specification of an assembly machine.
Figure 9.3 Typical “where used” report.
Figure 9.4 Typical summary report.
Figure 9.5 Explode report for the cashiers element, showing contents.
Figure 9.6 Status report showing pending events for model elements.
Figure 9.7 Model watch window ready for selection.
Figure 9.8 Model options/run window, with time scale factor highlighted.
Figure 9.9 Meteor trail dialog window.
Figure 9.10 Meteor trail display.
Figure 9.11 Preparing to activate the debugger on entry to actions.
Figure 9.12 The debugger window ready for use.
CHAPTER 10: SIMULATION PROJECT MANAGEMENT
Figure 10.1 A Gantt chart showing the major steps of a simulation study.
Figure 10.2 Conceptual validation, verification, and operational validation.
CHAPTER 11: MANUFACTURING SIMULATION CASE STUDIES
Figure 11.1 Macro-level schematic of TMP plant.
Figure 11.2 Fitting distributions to the collected data.
Figure 11.3 A snapshot of WITNESS
®
model for the hybrid simulation.
Figure 11.4 Baseline mag production time series graph.
Figure 11.5 Baseline time-in-state report chart.
Figure 11.6 Baseline time between VDP starts histogram.
Figure 11.7 Baseline time between histogram.
Figure 11.8 Baseline Mg production histogram.
Figure 11.9 A process map for SP3 paint process.
Figure 11.10 Airplane paint shop layout.
Figure 11.11 Process flow and base model.
Figure 11.12 Standalone throughput for paint operations.
Figure 11.13 Time-in-state chart for paint operations.
Figure 11.14 Process flow diagram and the data collected for the Hot mill area.
Figure 11.15 Process flow diagram and the data collected for the QA area.
Figure 11.16 Process flow diagram and the data collected for the finishing lines.
Figure 11.17 The WITNESS
®
simulation model of the seamless pipeline facility.
Figure 11.18 The simulation results of the baseline model.
Figure 11.19 The TIS graph of facility operations.
Figure 11.20 What-If #1 – line balanced schedule – throughput results.
Figure 11.21 What-If #2 – the crane at 50% – throughput results.
Figure 11.22 What-If #3 – crane at 75% – throughput results.
CHAPTER 12: SERVICE SIMULATION CASE STUDIES
Figure 12.1 Screenshot of harbor WITNESS
®
example.
Figure 12.2 Representation of the ship loading time via “option: min.”
Figure 12.3 Layout of the bank simulation example.
Figure 12.4 Summary of performance measure at different bank services.
Figure 12.5 A Process map of patients flow in the clinic example.
Figure 12.6 A flow diagram of the document issuance process.
Figure 12.7 The simulation model of the public service office.
CHAPTER 13: SIMULATION-BASED OPTIMIZATION METHODS
Figure 13.1 Illustration of simulation-based optimization.
Figure 13.2 Methods of simulation–based optimization.
Figure 13.3 Examples of WITNESS
®
Experimenter outputs.
Figure 13.4 Defining grinder cycle time using variables for ease of change.
Figure 13.5 Experimenter start-up window.
Figure 13.6 Experimenter experiment definition.
Figure 13.7 Definition of three scenarios.
Figure 13.8 Experimenter window with run length and replications.
Figure 13.9 Specification of a parameter.
Figure 13.10 Adding a basic response to be compared across scenarios.
Figure 13.11 First examination of scenario results.
Figure 13.12 Specification of changeover at polisher.
Figure 13.13 Advanced experiment design window.
Figure 13.14 Specification of a parameter, including step size.
Figure 13.15 Three parameters and a response now included.
Figure 13.16 Preparation for entering a constraint.
Figure 13.17 Editing a constraint.
Figure 13.18 Maximum grinder time is 4 more than minimum grinder time.
Figure 13.19 Numerical results from running nine scenarios.
Figure 13.20 Results chart.
Figure 13.21 WITNESS
®
optimization algorithm choices in the experimenter.
Figure 13.22 Pertinent details of adaptive thermostatistical SA algorithm.
Figure 13.23 The WITNESS
®
function “objective.”
Figure 13.24 Setting the objective within the experimenter.
Figure 13.25 Results of the experiment.
Figure 13.26 Box plots of the experiment results.
CHAPTER 14: SIMULATION FOR LEAN SYSTEMS
Figure 14.1 Key definitions of lean terminology.
Figure 14.2 Main VSM icons.
Figure 14.3 An example of a VSM.
Figure 14.4 A simulation-based lean approach.
Figure 14.5 A process map of the assembly process example.
Figure 14.6 Current state static VSM of the example assembly process.
Figure 14.7 DES model development for Lean.
Figure 14.8 Dynamic VSM for the current state assembly process.
Figure 14.9 Time-in-state statistics in the dynamic VSM.
Figure 14.10 Using a two-card Kanban system in the assembly process.
Figure 14.11 Lean-improved future state dynamic VSM.
Figure 14.12 Example 14.1 process flow.
Figure 14.13 WITNESS
®
model for Example 14.1.
CHAPTER 15: SIMULATION FOR SIX SIGMA
Figure 15.1 Highly capable process.
Figure 15.2 Marginally capable process
.
Figure 15.3 Incapable process
.
Figure 15.4 Six Sigma capable process (short-term).
Figure 15.5 Six Sigma capable process with long-term shift
.
Figure 15.6 Determining process Sigma rating.
Figure 15.7 The DMAIC Process.
Figure 15.8 WITNESS
®
Six Sigma designer elements.
Figure 15.9 A single machine process with defects.
Figure 15.10 A line of multiple serial operations with defects.
Figure 15.11 Conveyor network process model.
Figure 15.12 Current process CT distribution.
Figure 15.13 Improved process CT distribution.
Figure 15.14 Supply chain WITNESS
®
model.
Figure 15.15 Process flow of widget manufacturing.
Figure 15.16 The role of simulation in DMAIC.
Figure 15.17 A simulation-based DFSS- IDOV method.
Figure 15.18 QFD of the clinic example.
Figure 15.19 Clinic process map with CTQs.
Figure 15.20 Clinic P-Diagram.
Figure 15.21 The S-LSS integrated approach.
Figure 15.22 Application flow of the S-LSS.
List of Tables
CHAPTER 1: CONCEPTS OF SIMULATION MODELING
Table 1.1 Examples of Simulation Applications
Table 1.2 Examples of Simulated Systems
CHAPTER 2: WORLD-VIEWS OF SIMULATION
Table 2.1 Examples of Structural Elements in a Plant DES
Table 2.2 Data Collected at Various DES Elements
Table 2.3 Examples of Model Logic
Table 2.4 Examples of Model Statistics
Table 2.5 Examples of States of Different Model Elements
Table 2.6 Examples of System Resources
Table 2.7 Factors and Measures of System Resources
Table 2.8 Examples of System Logic
Table 2.9 Simulation Table of the First Hour of Simulation Time
Table 2.10 Simulation Table for the First 20 Cars
CHAPTER 4: BASIC WITNESS
®
MODELING TECHNIQUES
Table 4.1 Possible Interactions of Two Consecutive Machines
Table 4.2 Projectionist's Daily Schedule
Table 4.3 Animation Placements of the Labor (Projectionist) Icon
Table 4.4 Tanker Specifications for Extended Example
CHAPTER 5: MODELING MATERIAL HANDLING SYSTEMS
Table 5.1 Details of Cycles of Second Machine
CHAPTER 7: SIMULATION INPUT MODELING
Table 7.1 Summary of Commonly Used Distributions in Simulation Studies
Table 7.2 Simulation Data for the Auto Service Center
Table 7.3 Collected ER Data
Table 7.4 Summary of Fitted Distributions to Collected ER Data
CHAPTER 8: SIMULATION OUTPUT ANALYSIS
Table 8.1 Throughput distribution at selected time steps
Table 8.2 Statistical Methods and Modeling Techniques
Table 8.3 Examples of Parameters and Statistics
Table 8.4 Descriptive Statistics Summary of Throughput Data
Table 8.5 The two types of test errors
Table 8.6 Simulation Results for Testing Hypothesis
Table 8.7 Simulation DOE for a 2
3
Factorial Design
Table 8.8 Sampling Distributions in the Clinic Example
Table 8.9 Comparison of Two Clinic Scenarios
CHAPTER 10: SIMULATION PROJECT MANAGEMENT
Table 10.1 Contents of the Project Functional Specifications
Table 10.2 Contents of a Proposal for a Simulation Project
Table 10.3 Contents of a Final Project Report
CHAPTER 11: MANUFACTURING SIMULATION CASE STUDIES
Table 11.1 Summary of Data Inputs
Table 11.2 Summary of Data Outputs
Table 11.3 Baseline Plant Performance Summary
Table 11.4 Exp #1 Plant Performance Summary Table
Table 11.5 Exp #2 Plant Performance Summary Table
Table 11.6 Data Collected from Paint Capacity Study
Table 11.8 Data collected for Setup/Maintenance/Break Down
Table 11.9 KPIs Results
Table 11.10 A Snapshot of Model's Master Data Sheet
Table 11.11 A Snapshot of Model's Scheduled Data
CHAPTER 12: SERVICE SIMULATION CASE STUDIES
Table 12.1 Tanker Specifications for Extended Example
Table 12.2 Standard Distribution Fitted to Collected Bank Data
Table 12.3 Distribution of Clinic Staff
Table 12.4 Sampling Distributions in the Clinic Example
Table 12.5 Comparison of Two Clinic Scenarios
Table 12.6 Arrival Rates and Delay Times at the CAP Department
Table 12.7 Simulation Results (KPIs for the Current Status)
Table 12.8 Defined Design Variables for Optimizing the Public Service Office
Table 12.9 Bound constraints Defined on Various Waiting Times
Table 12.10 Simulation Results (KPIs for the Improved CAP Department)
Table 12.11 Percentage Improvement in KPIs After Improvement
CHAPTER 13: SIMULATION-BASED OPTIMIZATION METHODS
Table 13.1 Grinding Machine Alternatives for Scenario Manager
Table 13.2 Grinding Machine Alternatives with Two Parameters
CHAPTER 14: SIMULATION FOR LEAN SYSTEMS
Table 14.1 Major Benefits of Lean Techniques
Table 14.2 Details of DES Model Development Process
Table 14.3 Values of Current State Lean Measures
Table 14.4 Results of SMED Application to Line #4
Table 14.5 Summary of Lean-Improved Simulation Results
Table 14.6 Assessed PUSH/PULL Scenarios in Example 14.1
CHAPTER 15: SIMULATION FOR SIX SIGMA
Table 15.1 Main DMAIC Tools and Deliverables
Table 15.2 DMAIC-DES Interaction
Table 15.3 Data for Processes Operating Parameters
Table 15.4 Simulation Results Summary
Table 15.5 Sigma Calculation for ND and MLT
Table 15.6 Improving Process Sigma Based on ND
Table 15.7 Improving Process Sigma Based on MLT
Table 15.8 QFD-Based Clinic CTQs
Table 15.9 Simulation Results at Baseline Design
Table 15.10 CTQs Results of L
16
Experimental Design
Table 15.11 Simulation Results at the DOE Clinic Design #2
Table 15.12 Simulation Results at the Economical Clinic Design
Table 15.13 Simulation Results at the Final Clinic Design
Table 15.14 Multiple Simulation Replications at Final Clinic Design
Table 15.15 Current-State Six Sigma calculations
Table 15.16 DOE-Improved Six Sigma Calculations
Table 15.17 Lean-Improved Six Sigma Calculations
PROCESS SIMULATION USING WITNESS®
RAID AL–AOMAR
EDWARD J. WILLIAMS
ONUR M. ÜLGEN
Copyright © 2015 by John Wiley & Sons, Inc. All rights reserved
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Cataloging-in-Publication Data:
Al-Aomar, Raid.
Process simulation using WITNESS® / Raid A. Al-Aomar, Edward J. Williams, Onur M. Ülgen
pages cm
Includes bibliographical references and index.
ISBN 978-0-470-37169-5 (hardback)
1. Operations research–Data processing. 2. Stochastic processes–Computer simulation. 3. Industrial engineering–Graphic methods. 4. WITNESS (Computer file) I. Williams, Ed, 1944- II. Ülgen, Onur M., 1949- III. Title.
T57.5.A426 2015
658.4′013028553–dc23
2015004423
Cover image courtesy of Getty Images/iStockphoto © agsandrew
“To Ferial, Salma, Saja, Yaman, and Kenan.”
—Raid Al-Aomar
“To Ravi and Marcelo.”
—Edward J. Williams
“To My Parents and ˙Idil, Berkin and Cenk.”
—Onur M. Ülgen.
ABOUT THE COMPANION WEBSITE
This book is accompanied by a companion website:www.wiley.com/go/processsimulationusingwitness
The website includes the following:
Presentation slides for each chapter
WITNESS
®
software download
Camtasia audiovisual files of building models
PREFACE
Routine use of simulation to improve processes is rapidly approaching the half-century mark. Initially, simulation (and, in particular, discrete-event process simulation, the topic of this book) was most frequently applied to manufacturing operations. In the last several decades, simulation analyses have expanded broadly, and interestingly, into niches such as warehousing, logistics, supply-chain operations, health care, harbor and maritime operations, mining, hotel and restaurant management, and more. Concurrent with this expansion of simulation and its usage, software dedicated to simulation has steadily expanded from languages (e.g., SIMAN®, SIMSCRIPT, GPSS) to free-standing software packages (e.g., WITNESS®, SIMUL8®, ProModel®, AutoMod®). These simulation packages now interface readily with spreadsheets, databases, and statistical software. Furthermore, they support two- and three-dimensional animations built concurrently with building a simulation model, along with integrated modules for optimization and experimental design. WITNESS®, a powerful, easy-to-use package and hence a worthy competitor, is explained in this text.
Effective use of simulation has foundations much deeper than facility in using any one software tool. These foundations include understanding of how a simulation model attempts to accurately reflect the operation of a real system through time; therefore, a simulation model provides a behavioral dynamic movie, not just a snapshot, of the system it represents. The accuracy of this representation requires understanding of statistical concepts such as randomness, choice of statistical distributions, independence, correlation and autocorrelation, the importance of sample size, and the building of confidence intervals. This understanding is necessary to support the analyst's decisions concerning whether to use a steady-state or terminating model analysis, choice of simulation run length, and choice of the number of replications.
This book is intended for use either within a university discrete-event process simulation course (advanced undergraduate or graduate level) or for self-study to learn the concepts of simulation and the use of the WITNESS® simulation software. Accordingly, most chapters provide exercises for the reader and student. Uniquely among such books, it provides exposition of all of the following:
Role of simulation in Six Sigma projects.
Role of simulation in Lean Systems.
Simulation-based optimization.
Case studies in manufacturing.
Case studies in service industries.
Use of WITNESS
®
as a simulation environment.
Project management in the context of simulation projects.
Examples in each chapter on the methods and concepts presented.
The organization of the book is as follows:
Chapter 1 explains the concepts of simulation modeling, with emphasis on discrete-event (as opposed to continuous) modeling.
Chapter 2 compares and contrasts the various world views, and hence conceptual approaches, available to the simulation modeler.
Chapter 3 provides an overview and a “guided tour” of the WITNESS® modeling environment.
Chapter 4 covers basic WITNESS® modeling techniques; after studying this chapter, the reader or student will be able to build simple WITNESS® models.
Chapter 5 covers the modeling of material handling systems, introducing the powerful WITNESS® concepts of Paths, Conveyors, Vehicles, and Tracks.
Chapter 6 provides a rigorous statistical overview of the concepts whose understanding is necessary for accurate analysis of both model inputs and model outputs. Fundamental concepts reviewed and explained here include random variables (both discrete and continuous), point estimation, and estimation by construction of confidence intervals. The importance of sample size to estimation is emphasized.
Chapter 7 uses these concepts to cover simulation model input analysis in detail, including the importance and techniques of fitting closed-form distributions to observed data. This chapter also provides “checklists” of frequently and routinely needed input data in various contexts.
Chapter 8 covers simulation model output analysis extensively, including the distinction between terminating and steady-state models, point and interval estimation, experimental designs such as full and fractional factorial, and hypothesis testing.
Chapter 9 then shows how to use these techniques and others to undertake simulation model verification (does the model work as the modeler intends?) and validation (does the model accurately reflect the behavior of the real or proposed system?).
Chapter 10 discusses effective techniques for managing a simulation project; these techniques require clear communication among modelers, statistical analysts, engineers, and managers. The eight major phases of a properly managed and executed simulation project, and their interrelationships, are explained here.
Chapter 11 provides case studies exemplifying the use of simulation in manufacturing. When applied to manufacturing, simulation analyses help assess vitally important performance metrics such as JPH (jobs per hour), waiting times (both average and extreme), queue lengths (average and extreme relative to buffer capacities), and effects of downtime. One of these was undertaken at an automotive supply company, another at an aviation company, and yet another at a pipe manufacturing company.
Chapter 12 likewise provides case studies concerning the use of simulation in service industries. Service industries to which simulation has been vigorously applied include banking, food industry (both food markets and restaurants), health-care systems (doctors' and dentists' offices, clinics, and hospitals), telecommunication, transportation, and the insurance industry. The examples in this chapter include a car wash, an oil-tanker port, and a bank.
Chapter 13 discusses simulation-based optimization methods. The contributions which methods such as gradient estimation, random search, and tabu search can make to a simulation-based analysis are explained and compared. This chapter also presents more statistically based methods of seeking optima, such as design of experiments (DOE) and response surface methodology (RSM).
Chapter 14 discusses the use of simulation to build and enhance lean systems, explaining how it can help enhance performance metrics such as reducing the various wastes such as excess inventory, non-value-added motion, and idle time.
Chapter 15 discusses the interrelationships and synergy between simulation and Six Sigma. In particular, simulation analyses can identify and evaluate strategies for reduction of variability (e.g., in processing times and transit times); this reduction of variability is a central component of Six Sigma.
Raid Al-AomarEdward J. WilliamsOnur M. Ülgen
ACKNOWLEDGMENTS
The authors express gratitude to numerous colleagues, and the editors at John Wiley, for extensive help as this book developed. We also gratefully acknowledge the help and encouragement of the Lanner Group, the company which developed the WITNESS® software.
CHAPTER 1CONCEPTS OF SIMULATION MODELING
1.1 OVERVIEW
Driven by growing competition and globalization and to remain competitive, companies across the world strive to maintain high product and service quality, low production costs, short lead times, an efficient supply chain, and high customer satisfaction. To this end, companies often relay on traditional process improvement and cost reduction measures and adopt emerging initiatives of quality management, lean manufacturing, and Six Sigma. These initiatives are widely used for system-level design, improvement, and problem-solving with the aim of integrating continuous improvement into the company's policy and strategic planning.
Successful deployment of such initiatives, therefore, requires an accurate system-level representation of underlying production and business processes. Examples of process representation range from transfer functions to process mapping, flow-charting, modeling, and value stream mapping. Real-world production and business systems are, however, characterized by complexity and dynamic and stochastic behavior. This makes mathematical approximation, static representation, and deterministic models less effective in representing the actual system behavior. Alternatively, simulation facilitates better representation of real-world systems and its application for system-level modeling is increasingly used as a common platform in emerging methods of system design, problem-solving, and improvement.
Simulation modeling, as an industrial engineering (IE) tool, has undergone a tremendous development in the last decade. This development can be pictured through the growing capabilities of simulation software tools and the application of simulation solutions to a variety of real-world problems. With the aid of simulation, companies nowadays can design efficient production and business systems, troubleshoot potential problems, and validate/tradeoff proposed solution alternatives to improve performance metrics, and, consequently, cut cost, meet targets, and boost sales and profits.
WITNESS® simulation software is a modern modeling tool that has been increasingly utilized in a wide range of production and business applications. WITNESS® is mainly characterized by ease-of-use, well-designed simulation modules, and integrated tools for system analysis and optimization. WITNESS® is also linked to emerging initiatives of Six Sigma and Lean Techniques through modules that facilitate Sigma calculation and process optimization.
This book discusses the theoretical and practical aspects of simulation modeling in the context of WITNESS® simulation environment. This chapter provides an introduction to the basic concepts of simulation and clarifies the simulation role and rationale. This includes an introduction to the concept, terminology, and types of models, a justification for utilizing simulation in real-world applications, and a brief discussion on the simulation process. Such background is essential to establish a basic understanding of what simulation is all about and to understand the key simulation role in process engineering and emerging technologies.
1.2 SYSTEM MODELING
System modeling as a term includes two important commonly used concepts; system and modeling. It is imperative to clarify such concepts before attempting to focus on their relevance to the “Simulation” topic. This section will introduce these two concepts and provide a generic classification to the different types of systeml models.
1.2.1 System Concept
System thinking is a fundamental skill in simulation modeling. The word system is commonly used in its broad meaning in a variety of engineering and nonengineering fields. In simple words, a system is often referred to as a set of elements or operations that are logically related and effectively configured toward the attainment of a certain goal or objective. To attain the intended goal or to serve the desired function, it is necessary for the system to receive a set of inputs, process them correctly, and produce the required outcomes. To sustain such flow, a certain control is required to govern the system behavior. Given such definition, we can analyze any system (S) based on the architecture shown in Figure 1.1.
Figure 1.1 Definition of system concept.
El-Haik, B., Al-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
As shown in Figure 1.1, each system (S) can be mainly defined in terms of a set of Inputs (I) received by a Process (P) and transformed into a specific set of Outputs (O). The process consists of a set of system Elements or Entities (EN) that are configured based on a set of logical Relationships (RL). An overall Goal (G) is often defined to represent the purpose and objective of the system. To sustain a flawless flow and correct functionality of I–P–O, some kind of controls (C) is essentially applied to system Inputs, Process, and Outputs. Thus, building a system or a system model primarily requires the following:
1.
Defining the goal (
G
) or the overall system objective and relating system structure to the goal attainment.
2.
Specifying the set of outcomes (
O
) that should be produced and their specifications that result in attaining the specified Goal (
G
).
3.
Specifying the set of system Inputs (
I
) that are required in order to produce the specified system Outcomes (
O
) along with the specifications of these Inputs (
I
).
4.
Listing system entities
S
= (
EN
1
,
EN
2
,
EN
3
, …,
EN
n
) and defining the characteristics and the individual role of each entity (resources, storage, etc.).
5.
Setting the logical relationships (
RL
1
,
RL
2
,
RL
3
, …,
RL
m
) among the defined set of system elements to perform the specified process activities.
6.
Specifying the system Controls (
C
) and their role in monitoring the specifications of system Inputs (
I
) and Outputs (
O
) and adjusting the operation of the Process (
P
) to meet the specified Goal (
G
).
This understanding requires for any arrangement of objects to be called a system to be structured logically and to have an interaction that leads to a useful outcome. Transforming system inputs into desired outputs is often performed through system resources. Correct processing is often supported by controls and inventory systems to assure quality and maintain steady performance. This understanding of system concept is our gateway to the broader subject of system engineering. Examples of common real-life systems include classrooms, computer systems, factories, hospitals, and so on. In the classroom example, students are subject to various elements of the educational process (P) in the classroom, which involves attendance, participation in class activities, submitting assignments, passing examinations, and so on in order to complete the class with certain qualifications and skills. Applying the definition of system to the classroom example leads to the following:
1.
The overall system goal (
G
) is set to educate students on a certain subject and provide quality education to students attending classes.
2.
System Inputs (
I
) are students of certain age, academic level, major, and so on.
3.
System Outputs (
O
) are also students upon fulfilling class requirements.
4.
The set of system entities is defined as follows:
5.
The defined entities in
S
are logically related through a set of relationships (
RL
). For example, chairs are located around tables that face the instructor, the instructor stands in front of students and writes on whiteboard, and so on.
6.
Finally, class regulations and policies for admission, attendance, grading, and graduation represent process Controls (
C
).
It is worth mentioning that the term “system” covers both products and processes. A product system can be an automobile, a cellular telephone, a computer, a calculator, and so on. Any of these products involves the defined components of the system in terms inputs, outputs, elements, relationships, controls, and goal. Try to analyze all the mentioned examples from this perspective. On the other hand, a process system can be a manufacturing process, an assembly line, a power plant, and a business process. Similarly, any of these processes involves the defined components of the system. Try to analyze all the mentioned examples from this perspective.
1.2.2 Modeling Concept
The word modeling refers to the process of representing a system with a selected model that is easier to understand and less expensive to build compared to the actual system. The system model includes a representation of system elements, relationships, inputs, controls, and outputs. Modeling a system, therefore, has two prerequisites:
1.
Understanding the structure of the actual (real-world) system and the functionality of its components. It is imperative for the analyst to be familiar with the system and understand its purpose and functionality. For example, in an automobile assembly plant, the modeler needs to be familiar with the production system of building vehicles before attempting to model the vehicle assembly operations. Similarly, the modeler needs to be familiar with different types of bank transactions to develop a useful model of a bank.
2.
Being familiar with different modeling and system representation techniques and methods. This skill is essential to choose the appropriate modeling technique for representing the underlying real-world system under budgetary and time constraints. The selection of the most feasible modeling method is a decision of economy, attainability, and usefulness.
Modeling a system of interest is a combination of both art and science. It involves abstracting a real-world system into a clear, comprehensive, accurate, reliable, and useful representation. Such model can be used to better understand the system and to facilitate system analysis and improvement. As shown in Figure 1.2, the objects of the real-world system are replaced by objects of representation and symbols of indication. This includes the set of system entities (EN), relationships among entities (RL), system inputs (I), Controls (C), and system outputs (O). Actual system EN–RL–I–C–O is mimicked to a degree in the system model, leading to a representation that captures the characteristics of the real-world process for the purpose at hand. However, in a reliable model, this approximation should be as realistic as needed and should not overlook the key system characteristics.
Figure 1.2 The process of system modeling.
El-Haik, B., Al-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
1.2.3 Types of Models
Several modeling methods can be used to develop a system model. The analyst's choice of modeling method is based on several criteria including modeling objective, system nature and complexity, and the time and cost of modeling. As shown in Figure 1.3, we can classify the different types of models into four major categories: physical models, graphical models, mathematical models, and computer (logical) models. The following is a summary of those types of models.
1.
Physical models
: Physical models are tangible prototypes of the actual products or processes in a one-to-one scale or in any other feasible scale of choice. Such models provide a close-to-reality direct representation of the actual system to demonstrate its structure and functionality in a physical manner. They are common in large-scale engineering projects such as new car and airplane concepts, bridges, buildings, ships, and other architectural designs. They help designers better understand the system of interest and allow them to try out different configurations of design elements before the actual build up. Physical models may be built from clay or wood such as car prototypes or developed using 3D printing machines using different materials. Various techniques of rapid prototyping and reverse engineering are also used to develop product/process prototypes.
Figure 1.4
shows examples of product prototypes. Physical models can also be operational models such as flight simulators and real time simulators of chemical operations. Another form of physical models can be Lego-type machines, conveyor structures, and plant or reactor models. The benefit of physical models is the direct and easy to understand tangible representation of the underlying system. However, there are several limitations to physical models. The cost of physical modeling could be enormous in some cases. Some systems are too complex to be prototyped. Other physical models might be time consuming and require superior crafting skills to be built. For example, think of building a physical model for an internal combustion engine (ICE) or an assembly line of personal computers (PCs). What kind of cost, time, and skill would be involved in developing such prototypes?
2.
Graphical models
: Graphical models are abstracts of products or processes developed using graphical tools. These tools range from paper and pencil sketches to engineering drawings. Common graphical representations include process maps, flow and block diagrams, networks, and operations charts.
Figure 1.5
presents an example of a graphical model (operations chart of can opener assembly). The majority of graphical representations are static models that oversimplify the reality of the system and do not provide technical and functionality details of the process, which makes it difficult to try out what-if scenarios and to explain how the system responds to various changes in process parameters and operating conditions. Thus, graphical models as commonly used to develop physical and computer models.
3.
Mathematical Models
: Mathematical modeling is the process of representing the system behavior with formulas, mathematical equations, and calculus-based methods. They are symbolic representations of systems functionality, decision (control) variables, response, and constraints. Design formulas for stress-strain analyses, probabilistic and statistical models, and mathematical programming models are examples of mathematical models. Typical example of mathematical models includes using linear programming (LP) in capital budgeting, production planning, resources allocation, and facility location. Other examples include queuing models, Markov chains, and economic order quantity (EOQ) model. Some mathematical models can be also empirical models derived from regression analysis and transfer functions. Typically, a mathematical formula is a closed-form relationship between a dependent variable (
Y
) and one or more independent variables (
X
) with the form of
Y = f(X)
. Such a formula can be linear or nonlinear.
Figure 1.6
shows an example of a mathematical model built using MATLAB software. The dependent variable is often selected to measure a key characteristic of the system such as the speed of a vehicle or the yield of a process. Independent variables of the formula represent the key or critical parameters on whichsystem response depends such as time, distance, or force. Unfortunately, not all system responses can be modeled using mathematical formulas. Complexity of most real-world systems challenges the application of such models. Hence, a set of simplification assumptions often accompanies the application of mathematical models in order for the derived mathematical formulas to hold. For example, applying the EOQ inventory model assumes constant demand and lead-time. Such assumptions often lead to impractical results that have a limited chance of being implemented. For example, think of developing a formula that computes a production system throughput given parameters such as machine cycle times, speeds of conveyance systems, number of assembly operators, sizes of system buffers, and plant operating pattern. What kind of mathematical model would you use to approximate such response? How representative will the mathematical model be? Can you use the throughput numbers obtained from such a mathematical model to plan schedule deliveries to customers?
4.
Computer Models
: Computer models are numerical, graphical, mathematical, and logical representation of a system (a product or a process) that utilizes the capability of computers in fast computations, large capacity, consistency, animation, and accuracy. Computer simulation models, in particular, are virtual representations of real-world products and processes on the computer. Simulations of products and processes are developed using different application programs and software tools. For example, a computer program can be used to develop a finite element analysis (FEA) model to analyze stress and strains for a certain product design, as shown in
Figure 1.7
. Similarly, several mathematical models that represent complex mathematical operations, control systems, fluid mechanics, and others can be built, animated, and analyzed with computer tools. Software tools are also available to develop static and dynamic animations of many industrial processes.
Figure 1.3 Types of system models.
El-Haik, B., Al-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
Figure 1.4 Examples of product prototypes.
El-Haik, B., Al-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
Figure 1.5 Example of a process graphical model.
El-Haik, B., Al-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
Figure 1.6 An example of a MATLAB mathematical model.
El-Haik, B., Al-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
Figure 1.7 Example of a FEA computer model.
El-Haik, B., Al-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
Accurate and well-built computer models compensate for the limitations of the other types of models. They are typically easier, faster, and cheaper than building physical models. In addition, the flexibility and computation capability of computer models allow for making quick model changes, easy testing of what-ifs, and accurate evaluation of system performance for experimental design and optimization studies. Computer models also provide the benefits of graphical models through modern animation and graphical modeling tools. Compared to complex mathematical models, computer models are generally more realistic and efficient. They utilizel computer capabilities for more accurate approximations, they run tremendous computations in little time, and they can measure system performance without the need for a closed-form definition of the system objective function. Such capabilities made computer models the most common modeling techniques. Limitations of computer models stem from the limitation of computer hardware, the limitations of application software, the simulated system complexity and data availability, and the limited skillsof the simulation analyst in benefiting from the software features and in conducting the simulation analyses.
discrete event simulation (DES) is the type of computer simulation that mimics the operation of real-world processes as they evolve over time. The mechanism of DES computer modeling, discussed in Chapter 2, assists in capturing the dynamics and logics of system processes and estimating the system's long-term performance under stochastic conditions. Moreover, DES models allow the user to test various “what-if” system scenarios, make model changes to mimic potential changes in the physical conditions, and run the system many times for long periods to “simulate” the impacts of such changes. The model results are then analyzed to gain insight into the behavior of the system. For example, a DES plant model can be used to estimate the assembly line throughput by running the model dynamically and tracking its throughput hour-by-hour or shift-by-shift. The model can also be used to assess multiple production scenarios based on long-term average throughput. As shown in Figure 1.8, simulation software tools provide a flexible environment of modeling and analysis that makes DES more preferable compared to graphical, mathematical, and physical models.
Figure 1.8 Example of a DES model built-in WITNESS® software.
1.3 SIMULATION MODELING
Modeling, as shown earlier, is the art and science of capturing the functionality and the relevant characteristics of real-world systems. Modeling involves presenting such systems in a form that provides sufficient knowledge and facilitates system analyses and improvement. Physical, graphical, mathematical, and computer models are the major types of models developed for different purposes and applications. This section focuses on defining the simulation concept, developing a taxonomy of different types of simulation models, and explaining the role of simulation in planning, designing, and improving the performance of business and production systems.
1.3.1 Simulation Defined
Simulation is a widely used term in reference to computer models that represent physical systems (products or processes). It provides a simplified representation that captures important operational features of a real system. For example, FEA represents the mathematical basis for a camshaft product simulation. Similarly, production flow, scheduling rules, and operating pattern represent the logical basis for developing a plant process model.
System simulation model is the computer mimicking of the complex, stochastic, and dynamic operation of a real-world system (including inputs, elements, logic, controls, and outputs). Examples of system simulation models include mimicking the day-to-day operation of a bank, the production flow in an assembly line, or the departure/arrival schedule in an airport. As an alternative to impractical mathematical models or costly physical prototypes, computer simulation has made it possible to model and analyze real-world systems.
As shown in Figure 1.9, the primary requirements for simulation are: a system to be simulated, a simulation analyst, a computer system, and simulation software. The analyst has a pivotal role in the simulation process. He or she is responsible for understanding the real-world system (inputs, elements, logic, and outputs), developing a conceptual model, and collecting pertinent data. The analyst then operates the computer system and uses the simulation software to build, validate, and verify the system simulation model. Finally, the analyst analyzes simulation results and determines best process setting.
Figure 1.9 The simulation process.
El-Haik, B., Al-Aomar, R. (2006). Reproduced with permission of John Wiley & Sons, Inc.
Computer system provides the hardware and software tools required to operate and run the simulation model. The simulation software or language provides the platform and environment that facilitates model building, testing, debugging, and running. The simulation analyst utilizes the simulation software on a capable computer system to develop a system simulation model that can be used as a practical (close-to-reality) representation of the actual system.
1.3.2 Simulation Taxonomy
Based on the selected internal representation scheme, simulation models can be discrete, continuous, or combined
