MXB334 Operations Research for Stochastic Processes
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MXB334 |
|---|---|
| Prerequisite(s): | MXB232 and MXB241 |
| Equivalent(s): | MAB625 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $578 |
| Domestic tuition unit fee | $3,528 |
| International unit fee | $4,632 |
Unit Outline: Semester 2 2025, Gardens Point, Internal
| Unit code: | MXB334 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB232 and MXB241 |
| Equivalent: | MAB625 |
| Coordinator: | Paul Corry | p.corry@qut.edu.au |
Overview
This unit provides you with the opportunity to apply your knowledge and skills in operations research to guide decision-making for complex real-world problems. Your previous learning in deriving and solving operations research problems was mostly dealing with a decision making in a deterministic setting. The focus here is to optimize decision making when there is uncertainty and stochastic variables. Combined with the operations research expertise you have acquired over your degree, you will be able to formulate and solve these complex decision problems using computational tools.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of stochastic operations research techniques and their importance in modelling real-world problems.
- Critically select and apply advanced operations research techniques, tools and methods to analyse and solve problems, both familiar and unfamiliar, of both applied and abstract nature.
- Use computer-based techniques to solve operations research problems.
- Verify the effectiveness of a model and validate a proposed solution for a real-world problem.
- Effectively and coherently communicate the outcomes of modelling and analysis in oral and written form.
Content
You will also acquire new knowledge and state of the art skills based on a selection the following topics:
- Decision analysis, including decision making with and without experimentation;
- Simulation techniques including Monte-Carlo and Discrete Event Simulation
- Computational application of simulation techniques
- Queueing theory
- Formulating operations research questions from real life case studies.
- Interpreting operations research results, including project selection, cost effectiveness, and analysis of time and data availability.
Learning Approaches
This unit promotes experiential learning and involves 2 hours of lectures videos each week, 2 hours of interactive lecture, and 2 hours of practical workshop. During lecture videos, theory and concepts will be introduced, discussed, and some examples of their practical applications will also be presented. The interaction lecture session will reinforce these concepts with extra examples, context, and discussion.
Workshops will be focused on practical application of the theory and techniques covered during lectures. This will involve learning and applying operations research methods in the programming language Python. Delivery of the material presented will be context-based with examples from a range of real-world applications and purely mathematical scenarios. In this unit, the emphasis will be on learning by doing, learning in groups and as individuals, written and oral communication, and developing skills and attitudes to foster life-long learning skills. Creativity in problem-solving and critical assessment skills will be promoted with open discussions during workshops and interactive lectures.
You are expected to work in any contact session times allocated, but also in your own private study time. That is, you are expected to consolidate the material presented during class by working a wide variety of exercises, problems and online learning activities in your own time.
Feedback on Learning and Assessment
Formative feedback will be provided for the in-semester assessment items by way of written comments on the assessment items, student perusal of the marked assessment piece and informal interview as required.
Summative feedback will be provided throughout the semester with progressive posting of results via Canvas.
Assessment
Overview
The assessment items in this unit are designed to determine your level of competency in meeting the unit outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Quiz
This will evaluate your ability to independently apply simulation and theoretical techniques in a computational setting. You will have access to a computer during the quiz and will use software you have learnt during the unit to answer specific questions on the quiz.
Assessment: Case Study
The case study will emulate a real-life decision making exercise in a stochastic setting. You will be tasked to develop a high-quality project that would satisfy industry standards, demonstrate in depth knowledge of the unit concepts. You will perform simulations that can be used to gain insight and inform decision-making. You will present your findings and recommendations in a professional manner, as if to a group of experts in government, industry or academia, in report form, including the programming code developed for the project.
This is an assignment for the purposes of an extension.
Assessment: Examination (invigilated)
This will assess your knowledge and skills in using the techniques studied throughout this unit.
The examination will be at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide testing centre information.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
There are no set texts for this unit.
There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
1. Hillier FS (1995) Introduction to Operations Research, Oakland, Calif: Holden-Day
2. Winston WL (2004) Operations Research Applications and Algorithms, Boston: Duxbury Press
3. Taha HA (2010) Operations Research, An Introduction, New York: Macmillan
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres. Emergency exits and assembly areas will be pointed out in the first few lectures. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp
Unit Outline: Semester 2 2025, Online
| Unit code: | MXB334 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB232 and MXB241 |
| Equivalent: | MAB625 |
Overview
This unit provides you with the opportunity to apply your knowledge and skills in operations research to guide decision-making for complex real-world problems. Your previous learning in deriving and solving operations research problems was mostly dealing with a decision making in a deterministic setting. The focus here is to optimize decision making when there is uncertainty and stochastic variables. Combined with the operations research expertise you have acquired over your degree, you will be able to formulate and solve these complex decision problems using computational tools.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of stochastic operations research techniques and their importance in modelling real-world problems.
- Critically select and apply advanced operations research techniques, tools and methods to analyse and solve problems, both familiar and unfamiliar, of both applied and abstract nature.
- Use computer-based techniques to solve operations research problems.
- Verify the effectiveness of a model and validate a proposed solution for a real-world problem.
- Effectively and coherently communicate the outcomes of modelling and analysis in oral and written form.
Content
You will also acquire new knowledge and state of the art skills based on a selection the following topics:
- Decision analysis, including decision making with and without experimentation;
- Simulation techniques including Monte-Carlo and Discrete Event Simulation
- Computational application of simulation techniques
- Queueing theory
- Formulating operations research questions from real life case studies.
- Interpreting operations research results, including project selection, cost effectiveness, and analysis of time and data availability.
Learning Approaches
This unit promotes experiential learning and involves 2 hours of lectures videos each week, 2 hours of interactive lecture, and 2 hours of practical workshop. During lecture videos, theory and concepts will be introduced, discussed, and some examples of their practical applications will also be presented. The interaction lecture session will reinforce these concepts with extra examples, context, and discussion.
Workshops will be focused on practical application of the theory and techniques covered during lectures. This will involve learning and applying operations research methods in the programming language Python. Delivery of the material presented will be context-based with examples from a range of real-world applications and purely mathematical scenarios. In this unit, the emphasis will be on learning by doing, learning in groups and as individuals, written and oral communication, and developing skills and attitudes to foster life-long learning skills. Creativity in problem-solving and critical assessment skills will be promoted with open discussions during workshops and interactive lectures.
You are expected to work in any contact session times allocated, but also in your own private study time. That is, you are expected to consolidate the material presented during class by working a wide variety of exercises, problems and online learning activities in your own time.
Feedback on Learning and Assessment
Formative feedback will be provided for the in-semester assessment items by way of written comments on the assessment items, student perusal of the marked assessment piece and informal interview as required.
Summative feedback will be provided throughout the semester with progressive posting of results via Canvas.
Assessment
Overview
The assessment items in this unit are designed to determine your level of competency in meeting the unit outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Quiz
This will evaluate your ability to independently apply simulation and theoretical techniques in a computational setting. You will have access to a computer during the quiz and will use software you have learnt during the unit to answer specific questions on the quiz.
Assessment: Case Study
The case study will emulate a real-life decision making exercise in a stochastic setting. You will be tasked to develop a high-quality project that would satisfy industry standards, demonstrate in depth knowledge of the unit concepts. You will perform simulations that can be used to gain insight and inform decision-making. You will present your findings and recommendations in a professional manner, as if to a group of experts in government, industry or academia, in report form, including the programming code developed for the project.
This is an assignment for the purposes of an extension.
Assessment: Examination (invigilated)
This will assess your knowledge and skills in using the techniques studied throughout this unit.
The examination will be at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide testing centre information.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
There are no set texts for this unit.
There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
1. Hillier FS (1995) Introduction to Operations Research, Oakland, Calif: Holden-Day
2. Winston WL (2004) Operations Research Applications and Algorithms, Boston: Duxbury Press
3. Taha HA (2010) Operations Research, An Introduction, New York: Macmillan
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres. Emergency exits and assembly areas will be pointed out in the first few lectures. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp