APP MTH 3124 - Decision Science III
North Terrace Campus - Semester 2 - 2022
General Course Information
Course Code APP MTH 3124 Course Decision Science III Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites (MATHS 1012 or MATHS 1004) and (MATHS 2103 or MATHS 2107) Assumed Knowledge Proficiency in at least one of R, Python, MATLAB or Julia Course Description This course is focused on equipping students with simulation techniques to underpin decision making. Simulation is widely used to model systems, to evaluate risk, and to optimise objective functions, with the goal to inform decisions. Building up from uniform random generation, some of the key simulation techniques used for efficient simulation to support decision-making will be presented. Topics covered are: Uniform random number and random variable generation; random process generation; discrete-event simulation; basic statistical analysis of simulation data; variance reduction techniques; rare-event simulation; randomized optimization; applications in systems modelling, risk analysis and optimisation.
Course Coordinator: Professor Matthew RoughanThis course is focused on equipping students with simulation
techniques to underpin decision-making. Simulation is widely used to
model systems, to evaluate risk, and to optimise objective functions,
with the goal to inform decisions. Building up from uniform random
generation, some of the key simulation techniques used for efficient
simulation to support decision-making will be presented.
Students will be able to utilise: uniform random number and random
variable generation; random process generation; discrete-event
simulation; basic statistical analysis of simulation data;
applications in systems modelling and risk analysis.
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
Upon completion of this course, students will be able to:
communicate how randomness and controlled variation can be used to model complex systems in a range of application domains such as industry, health, and transportation.
create a model of a real-world problem specified in words and implement it as a discrete-event simulation.
validate results from a discrete-event simulation.
explore scenarios using simulation to elicit and compare possibilities.
systematically simulate to derive quantitative information with measures of confidence.
design simulation-based workflows to support decision-making in real-world contexts.
University Graduate Attributes
No information currently available.
Required ResourcesJulia programming language, v1.5 or later.
Recommended ResourcesAll assignments, tutorials, handouts and solutions, where appropriate, will be made available on MyUni as the course progresses.
Online LearningAll assignments, tutorials, handouts and solutions, where appropriate, will be made available on MyUni as the course progresses.
Learning & Teaching Activities
Learning & Teaching ModesThe course material largely appears in the form of short videos, and
pre-reading materials. Face-to-face sessions will focus on practicle
implementation of the ideas presented in this material. Students are
expected to participate in all sessions and online. Three main
assessments will form the basis of both learning and assessment -- for
first in the form of a sequence of quizzes, and the second and third in
larger staged activities.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Course content (videos, reading, external sources) 32 Practice and formative review 36 Practicals (including preparation time) 6 18 Assignments 3 70 Total 156
Learning Activities Summary
Introduction to using randomness in simulation
This subtopic will introduce the fundamentals of randomness in simulation -- why it's useful in data and decision science, and the mathematics underpinning it. It will set you up for future weeks by introducing random number, variable, and process generation.
- Discrete event simulation
Discrete event simulation (DES) is a cornerstone of simulation techniques for decision science. This subtopic will look at the applications of DES, and explore how to create your own DES. It will explain the assumptions and terminology and provide simple examples.
- Instrumentation, test and validation
A primary advantage of simulation is that it can generate data. But this is not useful unless one can (i) be confident that the simulation is doing what is expected, and (ii) systematically control the data being generated. This subtopic will consider how to instrument a simulation to obtain data, and how to check and validate that the simulation is doing what is expected.
The key advantage of simulation is the ability to test hypothetical situations, so-called “what if” scenarios. This subtopic will teach how to explore a parameter space of possibilities and compare results from simulations. We will revisit the questions of how to best model real situations. You will also examine some of the practical aspects of obtaining data from simulations.
Statistical analysis of simulation data
This subtopic will look at how to analyse the outputs of simulations statistically. It will cover appropriate summary statistics such as variance and some simple approaches to variance reduction. It will also cover some of the practical aspects of simulation such as burn in and parallelisation.
Applications to systems modelling and risk analysis
The course will finish with consideration of case studies of simulation applied to decision-making in industry.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment task Task type Weighting Learning outcomes Task 1 Ongoing quizzes 35% All Task 2 Implementation task 25% 1-4 Task 3 Project report 40% All
No information currently available.
No information currently available.
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.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
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.
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