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SAE 599 Modeling and Simulation for Systems Architecting and Engineering

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Title: SAE 599 Modeling and Simulation for Systems Architecting and Engineering


1
SAE 599 - Modeling and Simulation for Systems
Architecting and Engineering
  • Dr. Raymond Madachy
  • August 29, 2007

2
Outline
  • Class overview, schedule and logistics
  • info at DEN website http//den.usc.edu and
    http//csse.usc.edu/xx
  • System modeling and simulation overview
  • Basic terminology
  • Modeling process and heuristics
  • Demonstrations
  • Homework

3
Instructor Contact
  • Dr. Raymond Madachy
  • Email madachy_at_usc.edu
  • Office SAL 318 or GER 216C
  • Office Hours Wednesday, 430-630 pm
  • Phone 213.740.7275 (when working)

4
Course Overview
  • Cover modeling and simulation principles with
    applications to architecting and engineering of
    complex systems. You will use simulation tools
    and conduct studies to address current research
    issues for complex systems.
  • Modeling approaches focus on continuous and
    discrete simulation, but other types will also be
    described
  • We will survey applications for complex systems
    across a variety of engineering domains
  • Some of the lectures will be supplemented with
    demonstrations
  • Goal of the course for students is to understand
    how modeling and simulation can support the
    architecting process across a variety of domains,
    and be able to apply the techniques.

5
Course Objectives
  • Review basic simulation methods and principles
    applied to the architecting and engineering of
    complex systems. Describe the art and science of
    the modeling process, especially as applied to
    complex systems, and provide access to tools and
    executable models.
  • Cover continuous, discrete-event and other
    simulation methods. Students will learn to
    develop and execute their own simulation models.
  • Be exposed to a variety of simulation
    applications for system architecting and
    engineering disciplines by domain experts.
    Overview current and future research in the
    disciplines, and the future directions of
    modeling and simulation in general.
  • Develop simulation term projects that address
    critical research issues and/or industrial
    applications in systems architecting and
    engineering.

6
Schedule
7
Schedule (cont.)
8
Course Materials
  • Course text
  • Law M, Kelton W, Simulation Modeling and
    Analysis, 4th Edition McGraw-Hill, New York, NY,
    2006
  • Reference materials
  • Khoshnevis B, Systems Simulation -
    Implementations in EZSIM. McGraw-Hill, New York,
    NY, 1992
  • Madachy R. Software Process Dynamics, Wiley/IEEE
    Press, 2007
  • U.S. Defense and Modeling Office,
    http//www.dmso.mil, 2007
  • Selected papers and reading material will be
    provided to students during the class.
  • Tools and models as-provided

9
Grading
  • Grading breakdown
  • Midterm 15
  • Homework 20
  • Final examination 15
  • Course project 50
  • Homework assignments are announced in class.
    Off-campus students must submit their assignments
    in time to be received by DEN on the day they are
    due. Off campus assignments must be submitted as
    specified in the DEN guidelines. Assignments may
    be turned in late for partial credit. Credit
    decrements will increase 20 with each class
    period after the due date. All assignments must
    be prepared using a word processor, spreadsheet,
    presentation graphics, and simulation software as
    required.
  • Final Exam
  • The university schedules the final exam date and
    time. This semester the final exam is on December
    TBD, 2007.

10
Student Background Questions
  • What is your major and status?
  • What other SAE courses have you taken?
  • What simulation courses have you taken?
  • Describe your experience in modeling and
    simulation in industry.
  • How frequently will you watch the DEN webcast
    live?

11
My ISE Background
  • 1981-1988 Software development for aerospace
    real-time embedded systems and
    scientific programming, General Dynamics, Hughes,
    TRW
  • 1983 M.S. Systems Science, UCSD
  • 1988-1992 Software management for real-time
    displays and controls, program software
    metrics, Librascope
  • 1992-2000 Software process improvement,
    metrics, risk management, SEPG management,
    Litton Systems
  • 1994 Ph.D. Industrial and Systems
    Engineering, USC
  • 2000-2002 E-commerce software management, ROI
    and economic analysis, C-Bridge Institute
  • 2002-2005 Cost estimation, ROI and economic
    analysis, tool development, Cost Xpert Group
  • 2005Now Systems and software engineering
    research and project support, USC- CSE
  • Army Future Combat Systems
  • NASA Project Constellation

12
Software Process DynamicsRelevant Sections in
Course Reader
  • Part 1 - Fundamentals
  • Chapter 1 Introduction and Background
  • Chapter 2 The Modeling Process with System
    Dynamics
  • Chapter 3 Model Structures and Behavior for
    Software Processes
  • Part 2 Applications and Future Directions
  • Chapter 4 People Applications
  • Chapter 5 Process and Product Applications
  • Chapter 6 Project and Organization Applications
  • Appendix A Introduction to Statistics of
    Simulation

These chapters also available for students
interested in software-intensive systems
13
Term Projects
  • Simulation studies related to systems
    architecting and engineering that address
    critical research issues and/or industrial
    applications. The variety of topics is flexible,
    and each student will define his/her research
    topic to be addressed by modeling and simulation.
    In approved cases students can team up in groups
    of two.
  • Client/mentors available for some projects
    (encouraged).
  • Example available topics
  • Resource adaptability model for disasters and
    terrorist attacks (Jackson)
  • Motor sports simulation (Settles)
  • Limits to change on large SOS projects (Boehm,
    Lane)
  • Front-end GUI for web-based simulation tool
    (Madachy)
  • Acceptance test development for updated EZSIM II
    software (Madachy, Khoshnevis)
  • Carbon emissions model for China to LA cargo
    transport (Madachy, Bradbury-Huang)
  • Major exercises from Software Process Dynamics
    (Madachy)

14
Modeling Homework Guidelines
  • For all homeworks and projects
  • always show equations
  • liberally include comments in equations
  • provide rationale for numeric values and
    calibrations
  • show outputs for different simulation cases
  • discuss results

15
Outline
  • Class overview, schedule and logistics
  • info at DEN website http//den.usc.edu and
    http//csse.usc.edu/xx
  • System modeling and simulation overview
  • Basic terminology
  • Modeling process and heuristics
  • Demonstrations
  • Homework

16
Complex Systems, Modeling and Simulation
  • The evaluation of strategies for the architecting
    and engineering of complex systems involves many
    interrelated factors.
  • Effective systems, software and human engineering
    requires a balanced view of technology, business
    or mission goals, and people.
  • Modeling and simulation provides a framework to
    quantify the complex interactions and the
    strategy tradeoffs between cost, schedule,
    quality and risk.

17
Systems Engineering Challenges
  • What to build? Why? How well?
  • Stakeholder needs balancing, business case
  • Who to build it? Where?
  • Staffing, organizing, outsourcing
  • How to build? When in what order?
  • Construction processes, methods, tools,
    components, increments
  • How to adapt to change?
  • In user needs, technology, marketplace
  • How much is enough?
  • Functionality, quality, specifying, prototyping,
    test

18
Why Model Systems?
  • A system must be represented in some form in
    order to analyze it and communicate about it.
  • The models are abstractions of real or conceptual
    systems used as surrogates for low cost
    experimentation and study.
  • Models allow us to understand systems/processes
    by dividing them into parts and looking at how
    the parts are related.
  • We resort to modeling and simulation because
    there are too many interdependent factors to be
    computed, and truly complex systems cannot be
    solved by analytical methods.

19
Architected System Types
  • Hardware
  • Software
  • Human
  • Hardware-Software, Hardware-Human,
    Software-Human, Hardware-Software-Human
  • Living things (e.g. bio-computing)
  • Other?

20
Modeling Characterizations (Examples for
Software-Intensive System Processes from Madachy)
  • Purposes
  • Strategic management
  • Planning
  • Control and operational management
  • Process improvement and technology adoption
  • Training and learning
  • Scope
  • Portion of lifecycle
  • Development project
  • Multiple, concurrent projects
  • Long-term product evolution
  • Long-term organization

21
Modeling Applications (Examples for Weapons
Systems Acquisition Activities per DSMC)
  • Requirements definition
  • Program management
  • Design and engineering
  • Manufacturing
  • Test and evaluation
  • Logistics support
  • Training
  • Templates provided for these applications
  • Defense Systems Management College, System
    Acquisition Managers Guide for the Use of Models
    and Simulation, 1994

22
Outline
  • Class overview, schedule and logistics
  • info at DEN website http//den.usc.edu and
    http//csse.usc.edu/xx
  • System modeling and simulation overview
  • Basic terminology
  • Modeling process and heuristics
  • Demonstrations
  • Homework

23
Terminology
  • System a grouping of parts that operate together
    for a common purpose a subset of reality that is
    a focus of analysis
  • Open, closed
  • Static, dynamic
  • Characterized by 1) parameters that are
    independent measures that configure system inputs
    and structure, and 2) variables which depend on
    parameters and other variables. The collection
    of variables necessary to describe a system at
    any point in time is called the state of the
    system.
  • Model an abstract representation of reality.
  • Static, dynamic
  • Continuous, discrete
  • Deterministic, stochastic
  • Simulation the execution, or numerical
    evaluation of a mathematical model.

24
Terminology (cont.)
  • Discrete event simulation state variables change
    instantaneously at separate points in time
  • Continuous simulation state variables change
    continuously over time
  • System dynamics a simulation methodology for
    modeling continuous systems. Quantities are
    expressed as levels, rates and information links
    representing feedback loops.
  • Hybrid simulation contains both discrete-event
    and continuous model elements
  • Agent-based simulation a system is modeled as a
    collection of autonomous decision-making entities
    called agents. Each agent individually assesses
    its situation and makes decisions on the basis of
    a set of rules.

25
What Type of Model for What Type of System?
  • All classes of systems may be represented by any
    of the model types. A discrete model is not
    always used to represent a discrete system and
    vice-versa. The choice of model depends on the
    specific objectives of a study.

26
Outline
  • Class overview, schedule and logistics
  • info at DEN website http//den.usc.edu and
    http//csse.usc.edu/xx
  • System modeling and simulation overview
  • Basic terminology
  • Modeling process and heuristics
  • Demonstrations
  • Homework

27
Modeling Process Overview (from System Dynamics
paradigm)
  • Iterative, cyclic

policy implementation
system understandings
policy analysis
problem definition
simulation
model conceptualization
model formulation
28
Modeling Stages and Concerns (from System
Dynamics paradigm)
29
Like Architecting, Modeling is an Art and Science
30
Model Conceptualization Heuristics
  • Define a clear, operational purpose of the model.
  • Dont try to model the system.
  • Aggregate and abstract to the appropriate degree.
  • Use a top-down iterative approach.
  • KISS (keep it simple, stupid)

31
Model Formulation and Development Heuristics
  • Don't enumerate all factors at first.
  • Iteratively refine and slowly add relationships
    to model.
  • Normalize when possible.
  • Use relative measures.
  • Dont stray too far from a simulatable model.
  • Don't model in isolation try to involve those
    being modeled.

32
Model Validation Heuristics
  • Look for qualitative similarity on the first
    pass.
  • Alter one parameter at a time at first.
  • Be conscious of reality constraints.
  • Model validity is a relative matter.
  • The usefulness of a mathematical simulation model
    should be judged in comparison with the mental
    image or other abstract model which would be used
    instead Forrester 68. Models are successful if
    they clarify our knowledge and insights into
    systems.

33
Miscellaneous Heuristics
  • Data Collection
  • Model design should not be postponed until all
    pertinent parameters have been accurately
    measured.
  • Communication
  • Use simple diagrams to communicate with others
    until they seek more detail.

34
General Modeling Heuristics
  • No model is perfect.
  • but some are useful
  • All models are incomplete.
  • No model is final it is possible to build many
    different models of a single process.
  • All models contain hidden assumptions.
  • Continually challenge the model.

35
Outline
  • Class overview, schedule and logistics
  • info at DEN website http//den.usc.edu and
    http//csse.usc.edu/xx
  • System modeling and simulation overview
  • Basic terminology
  • Modeling process and heuristics
  • Demonstrations
  • Homework

36
Demonstrations
  • Continuous modeling with system dynamics

37
Outline
  • Class overview, schedule and logistics
  • info at DEN website http//den.usc.edu and
    http//csse.usc.edu/xx
  • System modeling and simulation overview
  • Basic terminology
  • Modeling process and heuristics
  • Demonstrations
  • Homework

38
Homework
  • Answer questions on your background
  • Read sections 1.1-1.4 and 1.9 of Law-Kelton
    textbook
  • Do a search for free public domain simulation
    software (possibly open-source or very low cost)
    discrete-event, continuous, hybrid, others
  • Find promising alternatives and do some limited
    testing
  • Write up your findings in 3-5 pages. List those
    you checked out, and develop a comparison matrix
    of features for promising candidates. Show
    simple models if possible. We are particularly
    interested in packages you can use for this
    course. Make sure and provide the links.
  • You may come across simulation tools suitable for
    a term project as coordinated with your company.
  • If no better tools are identified, you will be
    provided the EZSIM discrete-event simulation
    software for DOS and buy a continuous simulation
    tool for 100 at student price
  • There is some flexibility if you have other tools
    at your disposal
  • Lets see what our new options are today
  • Some links
  • Simulation tools http//www.idsia.ch/andrea/simt
    ools.html
  • Free discrete-even simulation software
    http//www.topology.org/soft/sim.html
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