MXN431 Advanced Operations Research
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: | MXN431 |
|---|---|
| Other requisite(s): | Unit Coordinator approval |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,900 |
| International unit fee | $5,220 |
Unit Outline: Semester 1 2026, Gardens Point, Internal
| Unit code: | MXN431 |
|---|---|
| Credit points: | 12 |
| Other Requisite: | Unit Coordinator approval required |
| Coordinator: | Paul Corry | p.corry@qut.edu.au |
Overview
This unit develops advanced knowledge, skills and application in operations research. In this unit, you will acquire advanced knowledge on how to adapt and implement general optimisation algorithms and meta-heuristics for operations research problems that arise in practical settings. Application contexts may include manufacturing and supply chains; transportation and engineering; rostering and planning; and ecological and natural resource management. This advanced unit is also designed to complement a research project in operations research and aims to prepare you for a career in industry or for further research studies at Masters or PhD level.
Learning Outcomes
On successful completion of this unit you will be able to:
- Employ advanced knowledge of optimisation and meta-heuristics necessary for advanced applications in economics, scientific, medical, health, environmental and/or engineering problems.
- Engage with end-users to understand their optimisation objectives and constraints, and to formulate a model of their system dynamics.
- Formulate and apply advanced computational optimisation approaches to offer useful solutions to these problems.
- Demonstrate advanced practical ability to employ optimisation and meta-heuristics.
- Summarise and explain the application of optimisation and meta-heuristics in written and/or oral form.
Content
This unit is designed to allow you to develop and practice operations research skills by considering topical areas of application.
The unit will be comprised of content selected from the following (or related) topics:
1. Continuous Function Optimisation Techniques
2. Combinatorial Problems
3. Approximate Computational Optimisation
4. Meta-heuristics
5. Stochastic Dynamic Programming
6. Multi-objective Optimisation
7. Research prioritisation approaches
Learning Approaches
The teaching and learning approaches will foster both acquisition of new knowledge at an advanced level and development of your skills. The approaches aim to facilitate your individual understanding of the key concepts and issues that are common to operations research in extended and complex problems and applications. Strategies also aim to assist you to develop your professional and life-long learning skills within selected realistic contexts that provide demonstration of key issues and methodology. Teaching and learning activities include problems in context and case studies to facilitate your discussion and to provide a learning environment in which you can develop your problem-solving skills.
You are expected to work not only in any lecture/workshop session times allocated, but also in your own private study time. That is, you are expected to consolidate the material presented by working through a wide variety of exercises, problems and online learning activities in your own time.
Feedback on Learning and Assessment
Feedback will be provided by academic staff through summative assessment tasks and through formative feedback during timetabled classes. You will also have opportunities for peer-to-peer learning and self-reflection to support your learning and skills development.
Assessment
Overview
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Case Study
This case study will focus on a challenging deterministic decision problem from a real-world industry, logistics or environmental management setting. You will be required to synthesise a realistic optimisation problem in mathematical form, develop computer code to implement an optimisation technique (approximate or exact) to generate optimal or near-optimal solutions to given problem instances and write a technical report that describes your methodology and summarises key results and findings. This authentic assessment closely simulates day-to-day tasks of operations research analysts working in both research and industry.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Invigilated Exam
This examination will focus on one or more decision problems from an industry, logistics or environmental management setting. This problem may include complicating aspects such as stochastic variables or multiple objectives. You will be required to formulate the problem in mathematical form and apply an appropriate technique to generate optimal solutions to the problem. An analysis of the generated solutions will be required to identify insightful commonalities and patterns. Solutions and methodology developed will be submitted via the learning management system. This assessment incorporates a verified identity component to be completed on-campus during class time.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
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
Risk Assessment Statement
There are no extraordinary risks associated with the classroom/lecture activities in this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.MS10 Bachelor of Mathematics (Honours)
- Demonstrate and apply advanced knowledge and skills in mathematical sciences to critically analyse and solve complex problems within the discipline or in cross-disciplinary fields where mathematics underpins innovation
Relates to: Case Study, Invigilated Exam - Communicate complex concepts, methods and findings in the mathematical sciences clearly and effectively to a range of audiences including mathematicians, industry professionals and the general public, using a range of academic, professional, and technical formats
Relates to: Case Study, Invigilated Exam - Demonstrate autonomy, accountability, ethical scholarship, and effective collaboration for research and continuous learning, consistent with professional practice in the mathematical sciences
Relates to: Case Study