MXB109 Introductory 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: | MXB109 |
|---|---|
| Equivalent(s): | MAB315, MXB232 |
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $4,872 |
Unit Outline: Semester 2 2026, Gardens Point, Internal
| Unit code: | MXB109 |
|---|---|
| Credit points: | 12 |
| Equivalent: | MAB315, MXB232 |
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100. |
Overview
Operations Research (OR) is a mathematics discipline focused on decision-making. Operations research provides foundation and methods to determine how best to design, operate, manage, and predict behaviour of complex systems. The cornerstone of operations research is formulating and solving mathematical and computational models to find optimal decisions. This unit is students' first opportunity to explore foundational operations research methods and techniques to solve management and optimisation problems.
In this unit we provide the theoretical foundation for future studies in operations research, building upon students' growing knowledge of linear algebra. This unit aims to develop students’ ability to apply various operations research methods, algorithms, and techniques to tackle practical, real-world problems in contexts such as the environment, agriculture, industry, finance, and healthcare.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of the principles, concepts and techniques in the field of Operations Research.
- Formulate a real-life problem in mathematical terms and apply operations research techniques, tools and methods to analyse and solve it.
- Use mathematical software to explore and solve introductory operations research problems
- Use critical thinking skills to select and apply appropriate operations research methods to analyse and solve problems, both familiar and unfamiliar, in a variety of fields both applied and abstract.
- Communicate the solution to a problem both quantitatively and qualitatively, for both mathematical and non-mathematical audiences.
Content
Deterministic methods and models for operations research. Fundamentals of linear programming. Formulation of linear programming problems. Solution of linear programming problems using graphical method. The simplex method. Analysis of linear programming models and their outputs, including sensitivity analysis. Network analysis. Transportation and assignment problems. Linear programming with multiple decision-makers. Computational approaches to solving linear programming problems and analysis of outputs. Indigenous perspectives are included through an optimisation problem relating to Torres Strait Islander culture and cuisine. Sustainability is addressed through algorithms for optimising resource use and resource efficiency.
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You will be provided with learning resources including pre-recorded videos, readings and formative quizzes that you can access flexibly to prepare for your timetabled learning activities. The pre-recorded videos will provide you with theoretical background and concepts applied in problem solving processes, and the formative quizzes are for you to check your understanding of the new concepts.
The timetabled sessions are an important opportunity for you to interact directly with the teaching team and ask for help or clarification when needed. The timetabled interactive lecture sessions will emphasise important concepts and work through additional example problems relevant for your assessment. In the timetabled workshops you will solve a range of example problems, from purely mathematical exercises to real-world applications.
You can expect to spend 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
As a first year unit, your learning will be carefully guided and scaffolded by the teaching staff, but you will be expected to develop some self-directed learning capabilities to facilitate your transition from dependent to independent learner across the degree. Additional free support relating to this unit is available through the STIMulate peer program.
Feedback on Learning and Assessment
Formative feedback will be provided for the in-semester assessment items through 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 results posted on 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: Problem Solving Task
This assessment will be completed individually. The task will assess early weeks of the unit. This will give you an indication of your progress in this unit. There will be around 4 pages of questions which will be of a similar nature to those presented in the lecture and workshops.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.
Assessment: Case Study
This assessment item will require you to analyse two real-world case studies and use linear programming techniques and common extensions to linear programming to formulate, solve, and analyse problems derived from real-world decisions, which include aspects of sustainability. You will write a technical report for each problem, covering the mathematical and computational methods used and the results obtained.
Before undertaking this assessment, you will review an example case study that incorporates Indigenous Knowledge. This example will give you clear guidance on the requirements for this assessment, and also introduce you to relevant Indigenous Perspectives.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.
Assessment: Final Exam
This supervised examination will assess your knowledge and skills in using the techniques studied throughout the unit.
The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.
The examination will require attendance 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 local 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 is no set text for this unit, although the following recommended text may be consulted as a complement to the lecture notes (optional).
There are many other 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.
Resource Materials
Recommended text(s)
Hillier, F.S. and Lieberman, G.J. (2014), Introduction to Operations Research, McGraw Hill.
Reference book(s)
Taha, H.A. ( 2006), Operations Research. An Introduction, Prentice Hall.
Winston, W.L. (2004), Operations Research Applications and Algorithms, Boston: Duxbury Press
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
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Problem Solving Task, Case Study, Final Exam - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Problem Solving Task, Case Study, Final Exam - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Problem Solving Task, Case Study, Final Exam - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Case Study, Final Exam
MS01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Problem Solving Task, Case Study, Final Exam - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Case Study, Final Exam - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Case Study, Final Exam - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Case Study, Final Exam - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Case Study, Final Exam - Demonstrate awareness of the social and ethical frameworks within which mathematics and statistics are practised, including their relation to Indigenous Australians and their impact on sustainability.
Relates to: Case Study
MV01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Problem Solving Task, Case Study, Final Exam - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Case Study, Final Exam - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Case Study, Final Exam - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Case Study, Final Exam - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Case Study, Final Exam - Demonstrate awareness of the social and ethical frameworks within which mathematics and statistics are practised, including their relation to Indigenous Australians and their impact on sustainability.
Relates to: Case Study
Unit Outline: Semester 2 2026, Online
| Unit code: | MXB109 |
|---|---|
| Credit points: | 12 |
| Equivalent: | MAB315, MXB232 |
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100. |
Overview
Operations Research (OR) is a mathematics discipline focused on decision-making. Operations research provides foundation and methods to determine how best to design, operate, manage, and predict behaviour of complex systems. The cornerstone of operations research is formulating and solving mathematical and computational models to find optimal decisions. This unit is students' first opportunity to explore foundational operations research methods and techniques to solve management and optimisation problems.
In this unit we provide the theoretical foundation for future studies in operations research, building upon students' growing knowledge of linear algebra. This unit aims to develop students’ ability to apply various operations research methods, algorithms, and techniques to tackle practical, real-world problems in contexts such as the environment, agriculture, industry, finance, and healthcare.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of the principles, concepts and techniques in the field of Operations Research.
- Formulate a real-life problem in mathematical terms and apply operations research techniques, tools and methods to analyse and solve it.
- Use mathematical software to explore and solve introductory operations research problems
- Use critical thinking skills to select and apply appropriate operations research methods to analyse and solve problems, both familiar and unfamiliar, in a variety of fields both applied and abstract.
- Communicate the solution to a problem both quantitatively and qualitatively, for both mathematical and non-mathematical audiences.
Content
Deterministic methods and models for operations research. Fundamentals of linear programming. Formulation of linear programming problems. Solution of linear programming problems using graphical method. The simplex method. Analysis of linear programming models and their outputs, including sensitivity analysis. Network analysis. Transportation and assignment problems. Linear programming with multiple decision-makers. Computational approaches to solving linear programming problems and analysis of outputs. Indigenous perspectives are included through an optimisation problem relating to Torres Strait Islander culture and cuisine. Sustainability is addressed through algorithms for optimising resource use and resource efficiency.
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You will be provided with learning resources including pre-recorded videos, readings and formative quizzes that you can access flexibly to prepare for your timetabled learning activities. The pre-recorded videos will provide you with theoretical background and concepts applied in problem solving processes, and the formative quizzes are for you to check your understanding of the new concepts.
The timetabled sessions are an important opportunity for you to interact directly with the teaching team and ask for help or clarification when needed. The timetabled interactive lecture sessions will emphasise important concepts and work through additional example problems relevant for your assessment. In the timetabled workshops you will solve a range of example problems, from purely mathematical exercises to real-world applications.
You can expect to spend 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
As a first year unit, your learning will be carefully guided and scaffolded by the teaching staff, but you will be expected to develop some self-directed learning capabilities to facilitate your transition from dependent to independent learner across the degree. Additional free support relating to this unit is available through the STIMulate peer program.
Feedback on Learning and Assessment
Formative feedback will be provided for the in-semester assessment items through 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 results posted on 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: Problem Solving Task
This assessment will be completed individually. The task will assess early weeks of the unit. This will give you an indication of your progress in this unit. There will be around 4 pages of questions which will be of a similar nature to those presented in the lecture and workshops.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.
Assessment: Case Study
This assessment item will require you to analyse two real-world case studies and use linear programming techniques and common extensions to linear programming to formulate, solve, and analyse problems derived from real-world decisions, which include aspects of sustainability. You will write a technical report for each problem, covering the mathematical and computational methods used and the results obtained.
Before undertaking this assessment, you will review an example case study that incorporates Indigenous Knowledge. This example will give you clear guidance on the requirements for this assessment, and also introduce you to relevant Indigenous Perspectives.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.
Assessment: Final Exam
This supervised examination will assess your knowledge and skills in using the techniques studied throughout the unit.
The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.
The examination will require attendance 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 local 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 is no set text for this unit, although the following recommended text may be consulted as a complement to the lecture notes (optional).
There are many other 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.
Resource Materials
Recommended text(s)
Hillier, F.S. and Lieberman, G.J. (2014), Introduction to Operations Research, McGraw Hill.
Reference book(s)
Taha, H.A. ( 2006), Operations Research. An Introduction, Prentice Hall.
Winston, W.L. (2004), Operations Research Applications and Algorithms, Boston: Duxbury Press
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
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: Problem Solving Task, Case Study, Final Exam - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Problem Solving Task, Case Study, Final Exam - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Problem Solving Task, Case Study, Final Exam - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Case Study, Final Exam
MS01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Problem Solving Task, Case Study, Final Exam - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Case Study, Final Exam - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Case Study, Final Exam - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Case Study, Final Exam - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Case Study, Final Exam - Demonstrate awareness of the social and ethical frameworks within which mathematics and statistics are practised, including their relation to Indigenous Australians and their impact on sustainability.
Relates to: Case Study
MV01 Bachelor of Mathematics
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the applied mathematical sciences, with depth in at least one area.
Relates to: Problem Solving Task, Case Study, Final Exam - Formulate and model problems in mathematical terms and apply appropriate mathematical, statistical and computational techniques to solve practical and abstract problems.
Relates to: Problem Solving Task, Case Study, Final Exam - Demonstrate aptitude in computer programming, and familiarity with industry-leading programming languages and relevant specialised mathematical, statistical and generative artificial intelligence software and tools.
Relates to: Case Study, Final Exam - Demonstrate critical thinking and problem solving skills across a range of applied mathematical and statistical contexts, and adaptivity in applying learned techniques in new or unfamiliar contexts.
Relates to: Case Study, Final Exam - Present information and articulate arguments and conclusions in a variety of modes, to diverse audiences both expert and non-expert.
Relates to: Case Study, Final Exam - Demonstrate awareness of the social and ethical frameworks within which mathematics and statistics are practised, including their relation to Indigenous Australians and their impact on sustainability.
Relates to: Case Study