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Modeling & Simulation Life Cycle. Life Cycle and Case Study. M&S Life Cycle. So far, we’ve focused primarily on issues concerning model execution This is only one aspect of a simulation study The M&S life cycle refers to the steps that take place during the course of a simulation study

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Modeling & Simulation Life Cycle

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Modeling & Simulation Life Cycle

Life Cycle and Case Study


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M&S Life Cycle

  • So far, we’ve focused primarily on issues concerning model execution

  • This is only one aspect of a simulation study

  • The M&S life cycle refers to the steps that take place during the course of a simulation study

  • In practice, not all of these steps are always followed, but one should keep in mind the overall process and be aware of potential consequences when certain steps are skipped


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Life Cycle for a Simulation Study

  • Problem formulation

  • Develop conceptual model, collect data

  • Validate conceptual model

  • Develop simulation

  • Verify, validate simulation

  • Design experiments

  • Make production runs

  • Analyze output data

  • Document, present results

Reference: A. M. Law and W. D. Kelton, Simulation Modeling and Analysis, Third Edition, McGraw Hill, 2000.


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Case Study

  • Air traffic control study performed by Mitre Corp. for the Federal Aviation Administration

    • Studies used to plan changes in the structure and/or management of the National Airspace System (NAS)

      • New airports or runways

      • Changes in operating procedures

  • Reference: William W. Trigeiro, “Impact of Regional Jets on Congestion in the NAS,” Mitre Center for Advanced Aviation System Development (CASSD), MP98W0000256V3, February 1999.

  • Software

    • Detailed Policy Assessment Tool (DPAT)

    • Georgia Tech Time Warp (GTW)


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Problem Formulation

  • State objectives, questions to be answered

    • Need agreement by analysts and policy makers

    • Define performance metrics

    • Develop overall plan, schedule, milestones

  • Case study

    • Increased use of regional jets (RJs) rather than propeller-driven aircraft (props) by airlines

      • Aircraft w/ 32-90 seats, typically used by regional airlines

      • Preferred by travelers: faster, quieter, perceived quality/safety

    • RJs fly at higher altitudes, compete with regular jets for airspace

    • Goal: “provide a ‘heads up’ to aviation planners… through 2003” of impact of RJs on congestion in the NAS

    • Two phase plan

      • Analyze airline industry to assess current and future use of RJs to develop scenario for December 2003 time frame

      • Construct airline schedule based on scenario and perform simulation study to evaluate impact on congestion


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Conceptual Model & Data Collection

  • Develop conceptual model of system, analyze current and planned practices and system configurations

    • Understand existing system and operating procedures as well as changes under consideration

    • Validate conceptual model (e.g., walk through with analysts, managers, subject matter experts)

  • Case Study

    • Analysis of airline industry

      • Start with existing airline schedules (Feb ‘98)

      • Literature search, airline announcements, plans of RJ manufacturers, FAA forecasts

      • Pilot union contracts restrict number and usage of RJs by airline, limiting growth of RJ traffic

      • Projected 800 RJs in use by 2003

    • Projected RJ schedules

      • Airlines would not release specific plans on RJ usage, retirement of prop aircraft; assume current strategies for RJ use remain valid

      • Replace turbo props on existing routes with RJs

      • Add new routes to “hub-and-spoke” structure exploiting increased speed and range of RJs


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Conceptual Model (cont.)

  • Case Study

    • Simulation model

      • Variety of simulation models exist

        • Detailed models capturing behavior of individual aircraft

        • Coarser queueing-based models of NAS

      • Queueing-based models used for this study

    • Conceptual model validation (partial)

      • Tracked actual RJ procurement plans of airlines

      • >200 RJs in service, firm orders for 500, options for 660 in 1999

      • Concluded aggregate model traffic load is conservative

      • More uncertainty in projected future RJ routes and schedules


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Simulation Software

  • Software options

    • Program in general purpose language (e.g., C or C++)

    • Simulation software package (e.g., Arena)

  • Case study

    • Several simulation packages already exist

      • Mitre - Detailed Policy Assessment Tool (DPAT)

      • Preston Group - Total Airspace and Airport Modeler (TAAM)

      • Numerous others

    • DPAT used in this study

      • Designed to predict system-wide delays resulting from congestion, weather, system outages, growth of system

      • Built over Georgia Tech Time Warp parallel simulation executive

      • Typical scenario (a day) of NAS simulated in under a minute

        • 1250 Logical Processes (520 airports, 730 airspace sectors)

        • Approximately 500K events per run


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Verification and Validation

  • Verification: Is the simulation software correct?

    • Software debugging

  • Validation: Does the simulation model give an accurate representation of the modeled system?

    • Do the outputs “make sense”?

    • Calibrate: iterative procedure involving comparing model predictions with an operational systems (or another model of the operational system), and modifying (tuning) model to improve accuracy

    • Sensitivity analysis: determine which factors have a large effect on performance measures

  • Case Study

    • Specifics not given (except growth in RJ use)

    • DPAT verified and validated separately


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Experiment Design

  • Specify what simulation runs are to be performed

    • Select scenarios, system configurations

    • Length of run; warm up period

    • Number of replications (independent runs) using different random number streams to establish confidence intervals

  • Case study

    • Scenarios using projected schedules for December 2003

    • Weather effects and other external events not simulated

    • Details of runs (number of trials, etc.) not specified


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Output Analysis

  • Output Analysis

    • Collect simulation results from sets of runs

    • What does it mean?

  • Case Study

    • Nine (out of 20) U.S. Air Route Traffic Control Centers (in Michigan/Florida/Texas triangle) will see traffic growth exceeding national averages

    • Largest growth in Indianapolis Center, then Cleveland and Dallas/Fort Worth

    • Significant congestion predicted in sectors north of Cincinnati

    • RJs will have minimal impact in western U.S.


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Concluding Remarks

  • A successful simulation study involves many other steps in addition to developing and running the simulation model

  • Inclusion of many individuals necessary to ensure the study is valid, and to promote “buy in” by decision makers and others

  • Don’t blindly believe the simulation results!


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