MXB233 Optimisation Modelling
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: | MXB233 |
|---|---|
| Prerequisite(s): | MXB109 or MXB232 |
| Equivalent(s): | MXB332 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
Unit Outline: Semester 1 2027, Gardens Point, Internal
| Unit code: | MXB233 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB109 or MXB232 |
| Equivalent: | MXB332 |
Overview
Operations research techniques are used in numerous industries and are critical for decision making. These industries need graduates who can apply techniques of mathematical modelling, statistical analysis, mathematical optimisation and simulation and can implement these techniques using appropriate computer software packages. This unit will build upon the introductory level operations research with more advanced “intermediate” level methods and techniques. The topics addressed in this subject are vital in this field and are critical for advanced applications and studies. Topics covered include: model building in mathematical programming, solution techniques for mathematical programming problems, computational solutions and their analysis using a software based solver - (e.g. OPL, Gurobi or equivalent), and applications for techniques covered.
Learning Outcomes
On successful completion of this unit you will be able to:
- Formulate a real-life problem in mathematical terms.
- Analyse and solve familiar and unfamiliar problems in a variety of fields using critical thinking skills to select and apply operations research techniques, tools and methods.
- Use computer based techniques to solve problems in operations research.
- Interpret published research of relevance to the unit and communicate solutions to problems appropriately.
- Collaborate in a team environment to create and present a report.
Content
The content of the unit includes:
- Model building in mathematical programming
- Mixed-Integer Programming (MIP) formulation
- Solution techniques for MIPs
- Binary variables and logical constraints
- Computer solutions and analysis of MIP Problems using a software-based solver
- Applications of MIPs
Learning Approaches
This unit is available for you to study in either on-campus or online mode. Learning in this unit includes weekly pre-recorded lectures, online activities, workshops, practicals and a unit communications channel, designed to facilitate communication with your peers and teaching staff outside of scheduled classes. You can expect to spend 10 - 15 hours per week involved in preparing for and attending all scheduled workshops, completing assessment tasks, and undertaking your own independent study to consolidate your learning.
The weekly pre-recorded lectures will introduce you to the theory and concepts and you will be exposed to the processes required to solve problems using the methods of this unit. There will also be a workshop and a practical session each week where consolidation of concepts and techniques presented in pre-recorded lectures will be achieved.
The material presented will be context-based utilising examples from a range of real-world applications and purely mathematical scenarios. The emphasis will be on learning by doing, learning in groups and as individuals, written and oral communication, and developing skills and attitudes to promote life-long learning. Operations research computer packages will be used for quick connection of theory and practice. Real-life case studies from different industry sectors will be provided as assignments. Indigenous perspectives are included through a worksheet on Indigenous Australian Land Management. Creativity in problem-solving and critical assessment skills will be promoted with open discussions.
Feedback on Learning and Assessment
Feedback in this unit is provided to you in the following ways:
- Ongoing formative feedback will be provided by your teaching staff during the workshop and practicals.
- Written feedback will be provided on summative assessment work tasks, which will be graded using rubrics.
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.
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.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Examination (open book)
This open book assessment item will assess your ability to read and interpret published research. It will provide you with feedback of your progress in this unit. In the lead up to the assessment, you will read and familiarise yourself with a scientific paper. On the day of the examination, you will be required to answer a series of (between 5 and 10) questions which may require mathematical calculations. The scope of answers should only be derived from information contained within the paper, or related content covered in this unit or general mathematical knowledge.
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.
Assessment: Case Study
For this assessment item you will develop a mathematical formulation of a real-life problem and use a computer to solve and analyse the problem. You will apply techniques covered in the unit such as integer programming. You are required to interpret a project brief which describes the case study, the scope of work you will undertake, and the deliverables to be submitted. The task will require abstraction of project brief information into the form of a mathematical optimisation model using integer programming techniques, solving the problem using computer software and the data provided, then presenting your results as a memo or short report.
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: Final Exam
This will assess your knowledge and skills in using the techniques studied throughout this unit.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
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. The late submission period does not apply, and no extensions are available. if you can’t attend this exam due to special circumstances, you may apply to sit a deferred exam.
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.
Highly recommended reference texts are listed below. There are also many online resources such as lecture notes and some e-books that can be found online.
Resource Materials
Reference book(s)
Hillier, F.S. (2009), Introduction to Operations Research, McGraw Hill.
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 the study of this unit.
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: Examination (open book), 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: Examination (open book), 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: Examination (open book), Case Study, Final Exam - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Case Study - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Examination (open book), 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: Examination (open book), 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: Examination (open book), 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 - 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: Examination (open book), 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 - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
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: Examination (open book), 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: Examination (open book), 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 - 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: Examination (open book), 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 - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Case Study
Unit Outline: Semester 1 2027, Online
| Unit code: | MXB233 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB109 or MXB232 |
| Equivalent: | MXB332 |
Overview
Operations research techniques are used in numerous industries and are critical for decision making. These industries need graduates who can apply techniques of mathematical modelling, statistical analysis, mathematical optimisation and simulation and can implement these techniques using appropriate computer software packages. This unit will build upon the introductory level operations research with more advanced “intermediate” level methods and techniques. The topics addressed in this subject are vital in this field and are critical for advanced applications and studies. Topics covered include: model building in mathematical programming, solution techniques for mathematical programming problems, computational solutions and their analysis using a software based solver - (e.g. OPL, Gurobi or equivalent), and applications for techniques covered.
Learning Outcomes
On successful completion of this unit you will be able to:
- Formulate a real-life problem in mathematical terms.
- Analyse and solve familiar and unfamiliar problems in a variety of fields using critical thinking skills to select and apply operations research techniques, tools and methods.
- Use computer based techniques to solve problems in operations research.
- Interpret published research of relevance to the unit and communicate solutions to problems appropriately.
- Collaborate in a team environment to create and present a report.
Content
The content of the unit includes:
- Model building in mathematical programming
- Mixed-Integer Programming (MIP) formulation
- Solution techniques for MIPs
- Binary variables and logical constraints
- Computer solutions and analysis of MIP Problems using a software-based solver
- Applications of MIPs
Learning Approaches
This unit is available for you to study in either on-campus or online mode. Learning in this unit includes weekly pre-recorded lectures, online activities, workshops, practicals and a unit communications channel, designed to facilitate communication with your peers and teaching staff outside of scheduled classes. You can expect to spend 10 - 15 hours per week involved in preparing for and attending all scheduled workshops, completing assessment tasks, and undertaking your own independent study to consolidate your learning.
The weekly pre-recorded lectures will introduce you to the theory and concepts and you will be exposed to the processes required to solve problems using the methods of this unit. There will also be a workshop and a practical session each week where consolidation of concepts and techniques presented in pre-recorded lectures will be achieved.
The material presented will be context-based utilising examples from a range of real-world applications and purely mathematical scenarios. The emphasis will be on learning by doing, learning in groups and as individuals, written and oral communication, and developing skills and attitudes to promote life-long learning. Operations research computer packages will be used for quick connection of theory and practice. Real-life case studies from different industry sectors will be provided as assignments. Indigenous perspectives are included through a worksheet on Indigenous Australian Land Management. Creativity in problem-solving and critical assessment skills will be promoted with open discussions.
Feedback on Learning and Assessment
Feedback in this unit is provided to you in the following ways:
- Ongoing formative feedback will be provided by your teaching staff during the workshop and practicals.
- Written feedback will be provided on summative assessment work tasks, which will be graded using rubrics.
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.
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.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Examination (open book)
This open book assessment item will assess your ability to read and interpret published research. It will provide you with feedback of your progress in this unit. In the lead up to the assessment, you will read and familiarise yourself with a scientific paper. On the day of the examination, you will be required to answer a series of (between 5 and 10) questions which may require mathematical calculations. The scope of answers should only be derived from information contained within the paper, or related content covered in this unit or general mathematical knowledge.
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.
Assessment: Case Study
For this assessment item you will develop a mathematical formulation of a real-life problem and use a computer to solve and analyse the problem. You will apply techniques covered in the unit such as integer programming. You are required to interpret a project brief which describes the case study, the scope of work you will undertake, and the deliverables to be submitted. The task will require abstraction of project brief information into the form of a mathematical optimisation model using integer programming techniques, solving the problem using computer software and the data provided, then presenting your results as a memo or short report.
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: Final Exam
This will assess your knowledge and skills in using the techniques studied throughout this unit.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
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. The late submission period does not apply, and no extensions are available. if you can’t attend this exam due to special circumstances, you may apply to sit a deferred exam.
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.
Highly recommended reference texts are listed below. There are also many online resources such as lecture notes and some e-books that can be found online.
Resource Materials
Reference book(s)
Hillier, F.S. (2009), Introduction to Operations Research, McGraw Hill.
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 the study of this unit.
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: Examination (open book), 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: Examination (open book), 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: Examination (open book), Case Study, Final Exam - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Case Study - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Examination (open book), 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: Examination (open book), 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: Examination (open book), 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 - 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: Examination (open book), 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 - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
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: Examination (open book), 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: Examination (open book), 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 - 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: Examination (open book), 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 - Work both independently and collaboratively in diverse teams, including cross-cultural and cross-disciplinary teams.
Relates to: Case Study