APP MTH 7054 - Modelling & Simulation of Stochastic Systems

North Terrace Campus - Semester 1 - 2015

The course provides students with the skills to analyse and design systems using modelling and simulation techniques. Case studies will be undertaken involving hands-on use of computer simulation. The application of simulation in areas such as manufacturing, telecommunications and transport will be investigated. At the end of this course, students will be capable of identifying practical situations where simulation modelling can be helpful, reporting to management on how they would undertake such a project, collecting relevant data, building and validating a model, analysing the output and reporting their findings to management. Students complete a project in groups of two or three, write a concise summary of what they have done and report their findings to the class. The project report at the end of this course should be a substantial document that is a record of a student's practical ability in simulation modelling. Topics covered are: Introduction to simulation, hand simulation and computer simulation , review of basic probability theory, introduction to random number generation, generation of random varieties, analysis of simulation output, variance reduction techniques and basic analytic queuing models.

  • General Course Information
    Course Details
    Course Code APP MTH 7054
    Course Modelling & Simulation of Stochastic Systems
    Coordinating Unit Applied Mathematics
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 2.5 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites MATHS 1012
    Assumed Knowledge At least 6 units of Level II Applied Mathematics courses
    Course Description The course provides students with the skills to analyse and design systems using modelling and simulation techniques. Case studies will be undertaken involving hands-on use of computer simulation. The application of simulation in areas such as manufacturing, telecommunications and transport will be investigated. At the end of this course, students will be capable of identifying practical situations where simulation modelling can be helpful, reporting to management on how they would undertake such a project, collecting relevant data, building and validating a model, analysing the output and reporting their findings to management. Students complete a project in groups of two or three, write a concise summary of what they have done and report their findings to the class. The project report at the end of this course should be a substantial document that is a record of a student's practical ability in simulation modelling.
    Topics covered are: Introduction to simulation, hand simulation and computer simulation , review of basic probability theory, introduction to random number generation, generation of random varieties, analysis of simulation output, variance reduction techniques and basic analytic queuing models.
    Course Staff

    Course Coordinator: Dr Andrew Coyle

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    In 2014 the topic of this course will be Modelling and Simulation of Stochastic Systems.

    Syllabus

    System modelling is the process of creating a model which imitates a system, generally in order to study some aspect of its operation.  Many models are created to study real world or proposed systems.  Some systems involve stochastic elements, in which there are many possible outcomes from the system, for example stock market prices, weather systems, or sports results. The main focus of this course will be on the use of simulation modelling for the modelling and investigation of these types of systems, although we will also look at some basic analytic mathematical models.  Both techniques provide the modeller with a rich set of tools, which may be used to model many systems of interest, for example manufacturing systems, telecommunications networks, financial systems, games, ecosystems, etc. A large part of the work for this course is to complete a group project that involves simulating a system chosen by the students. This involves the design, coding, analysis and reporting of the system being simulated. The Matlab programming language is used and the aspects of this language which are needed for the project will be taught.

    Learning Outcomes

    1) understand the basic concepts involved in designing a stochastic system model.
    2) use a simulation package or a simulation program (Matlab).
    3) present reults from a simulation, in both verbal and written formats.
    4) work as part of a team through an extended project.
    5) follow a system modelling and simulation exercise from conception through to completion.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1,2,3,4,5
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1,5
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 5
    Skills of a high order in interpersonal understanding, teamwork and communication. 3,4
    A proficiency in the appropriate use of contemporary technologies. 1,2,5
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1,5
  • Learning Resources
    Required Resources
    None.
    Recommended Resources
    1. Simulation, Sheldon Ross (Academic Press, 2006)
    2. Simulation Modelling and Analysis, Averill M. Law and W. David Kelton (McGraw Hill, 2000) 
    3. Simulation of Manufacturing Systems, A. Carrie (John Wiley & Sons, 1992)
    Online Learning
    This course uses MyUni exclusively for providing electronic resources, such as assignments and handouts, and for making course announcements. It is recommended that students make appropriate use of these resources. Link to MyUni login page: https://myuni.adelaide.edu.au/webapps/login/
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on lectures and computer sessions as the primary delivery mechanism for the material. A software project is a major part of this course. Computer sessions will be provided to assist in the planning and work for these projects. A couple of written assignments will help students to gauge their progress and understanding of the course.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity                            Quantity   Workload Hours
    Lectures and Practicals     30               90
    Project                                1               60
    Assignments                       2                 8
    Total                                                  158
    Learning Activities Summary
    Lecture and Computer Practicals Outline

    Week 1       Introduction to course and Matlab
    Week 2       Hand Simulation
    Week 3       Basic Simulation Techniques
    Week 4       Basic Probability Theory
    Week 5       Common Distributions
    Week 6       Real Data and Distributions
    Week 7       Project Review
    Week 8       Random Number Generation
    Week 9       Random Variate Generation
    Week 10     Elementary Analysis of Simulation Output
    Week 11     Project Presentations
    Week 12     Summary and Revision
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Component      Weighting           Objective Assessed
    Exam                70%                    all
    Project              30%                    all
    Assignments     None
    Assessment Detail
    There will be a single group project worth 30% of the total mark. The remaining 70% will come from the exam. The details of this project will be given out at the first lecture. Every week there will be a computer session in which help with with the project will be available. The lecturer will review the project in Week 7 to ensure that it is on a reasonable track. Compulsory group project presentations will take place in Week 11. The projects must be submitted electronically before the end of Week 12.

    There will be two exercises given out at Week 4 and Week 8. These are not compulsory but are exercises similar to the questions that will appear in the exam. Answers may be submitted the following week (Weeks 5 and 9) and will then be returned the next week (Weeks 6 and 10).
    Submission
    Assignments must be handed in person to the lecturer if they are to be marked.

    The project must be delivered electronically to the lecturer before the end of Week 12. The submission must include both the report and the corresponding Matlab code. Late submission of the project will invoke a penalty.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

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    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
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