Course coordinator
Professor Valentin Zelenyuk
Time and Location: TBA (see Blackboard for the course for thos and other updates)
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
Provides a working understanding of some of the principal techniques used in business decision making. Topics include linear programming, transportation and assignment models, project scheduling and control, inventory models, and decision theory and games. These techniques can be used to solve problems in areas as diverse as product mixing and blending, firm efficiency and benchmarking, project management, and multi-period financial planning. Problems and exercises are solved using Microsoft Excel or a simple menu-drive software package.
The quantitative techniques discussed in this course are collectively known as operations research (OR) or management science (MS) techniques. The OR/MS approach to business and economic decision making is scientific, relies on formal mathematical models, and involves the extensive use of computers. Students will learn how to use different types of software (Excel QM and Matlab) to implement the techniques taught in this course.
ﾠ
Course requirements
Assumed background
Students are expected to have a basic knowledge of economic theory, statistics and mathematics.ﾠ Before attempting this course, you are advised that it is important to complete the appropriate prerequisite course(s) listed on the front of this course profile. No responsibility will be accepted by UQ School of Economics, the Faculty of Business, Economics and Law or The University of Queensland for poor student performance occurring in courses where the appropriate prerequisite(s) has/have not been completed, for any reason whatsoever.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON7150 or MATH7050 or 7051 or 7052
Incompatible
You can't enrol in this course if you've already completed the following:
ECON2320
Course contact
School enquiries
Student Enquiries, School of Economics
Admin Enquiries - enquiries@economics.uq.edu.au
All enquiries regarding student and academic administration (i.e. non-course content information, e.g., class allocation, timetables, extension to assessment due date, etc.) should be directed to enquiries@economics.uq.edu.au.
Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Students are required to select their preferred class times through My Timetable and attend their allocated timeslot. The timetable is published through the UQ Public Timetable found in the APPs section of myUQ.ﾠ
Note: Tutorials start in teaching week 1.
All assessment dates are already scheduled (see Assessment section). Please write down all these dates in your personal diary at once. Block also in your diary the days during which you will need to revise to prepare for each assessment.
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
- Public Holidays: Wed 13 August (Royal Queensland Show Holiday), Mon 6 October (King’s Birthday public holiday).
- Mid-Semester Break: 29 September – 3 October. Semester 2 classes recommence on Tue 7 October.
Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up to date information, as from time to time late room changes may occur. .
Aims and outcomes
The aims of the course are to
- introduce a number of OR techniques which may be used for making decisions in business environments.
- present examples of decision-making environments in which each of these techniques can be applied.
- help students to gain an understanding of the limitations and potential uses of OR techniques in complex decision-making environments.
- provide basic familiarity with OR computer programs, and develop a capability to prepare data for input and interpret output from this software.
The course does not aim to develop skills in the development, testing and application of OR models for highly complex decision situations. However, the course does aim to give students an appreciation of where decision situations are of sufficient complexity to require the services of specialists in mathematical programming, systems simulation or other techniques.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Analyse relevant information about economic and business problems, and apply mathematical methods from Operations Research and Management Science to create models for them.
LO2.
Synthesise relevant information about economic and business problems, and solve mathematical models analytically to identify optimal solutions and perform sensitivity analysis.
LO3.
Analyse and synthesise relevant information about economic and business problems and use computer software to formulate and solve mathematical models in Operations Research and Management Science, conducting sensitivity analysis as part of the process.
LO4.
Analyse, synthesise, and evaluate the results of these estimations, effectively communicating the findings and their underlying logic through various digital communication and presentation tools.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Practical/ Demonstration, Quiz, Tutorial/ Problem Set |
Weekly Quizzes
|
40% best 7 out of 9 |
These quizzes will be held during and at the tutorials (in person mode) of Weeks 3, 4, 5, 6, 7, 8, 9, 11, 12. |
| Quiz |
Comprehensive Test
|
20% |
Due during and at the Tutorial of Week 13. |
| Computer Code, Creative Production/ Exhibition, Paper/ Report/ Annotation, Project | Business Improvement Research Project | 40% |
10/11/2025 2:00 pm |
Assessment details
Weekly Quizzes
- In-person
- Mode
- Activity/ Performance, Written
- Category
- Computer Code, Practical/ Demonstration, Quiz, Tutorial/ Problem Set
- Weight
- 40% best 7 out of 9
- Due date
These quizzes will be held during and at the tutorials (in person mode) of Weeks 3, 4, 5, 6, 7, 8, 9, 11, 12.
- Other conditions
- Time limited.
Task description
These Weekly Quizzes are to be done by each student individually, in person only, during and at the Tutorials to which the student is officially signed up.
These quizzes will be held during Weeks 3, 4, 5, 6, 7, 8, 9, 11, 12 (unless explicitly announced otherwise by the course coordinator via the blackboard). Best seven (out of the total nine weekly quizzes) will count towards the final grade.
Each quiz will be made available via Blackboard. (You must work in isolation while completing the quiz. Any direct or indirect collusion with others during this time may lead to charges of academic misconduct and a mark of zero for this assessment task.)
Each quiz involves multiple choice questions that generally need to be answered based on the material from prior lectures and tutorials. It will require analytic (mathematical) formulations of a business problem and computations and involving a use of the software or/and an interpretation of its output. It may also request submission of workings (e.g., as screenshots from the spreadsheet or a photo of derivations on the paper, etc.) The typical timing is about 20 minutes, typically at the beginning of the designated tutorial. The tutorial of Week 2 will have a trial quiz.
Note: This assessment task is to be completed in-person. The use of any generative Artificial Intelligence (AI) tools is not permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Electronically via Blackboard, in person, in the tutorial room.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
No deferral or extensions, but the students can drop or miss any two of the quizzes out of nine quizzes.
If all nine quizzes are taken, then the best seven will be counted.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Comprehensive Test
- In-person
- Mode
- Activity/ Performance
- Category
- Quiz
- Weight
- 20%
- Due date
Due during and at the Tutorial of Week 13.
- Other conditions
- Time limited.
Task description
This Test is to be done in person, individually, during and at the Tutorial of Week 13. It will be made available via Blackboard. (You must work in isolation while completing the Test. Any direct or indirect collusion with others during this time may lead to charges of academic misconduct and a mark of zero for this assessment task.) The test involves multiple choice questions that generally need to be answered based on the material from lectures and tutorials of Weeks 1-12, and may require solving problems via analytic (mathematical) formulations, derivations and computations and involving a use of the software or/and an interpretation of its output. More information regarding the structure of the test and how to prepare for it will be provided in lectures and/or Blackboard announcements in the weeks leading up to the test.
The students can prepare better for this test by answering the quiz questions during the lectures (in-person only) when these questions are prompted randomly by the lecturer. About 2-3 minutes will be given for answering each question. Note that these questions are similar to those that will be during the comprehensive test.
Note: This assessment task is to be completed in-person. The use of any generative Artificial Intelligence (AI) tools is not permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Electronically via Blackboard, in person, in the tutorial room.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Business Improvement Research Project
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Creative Production/ Exhibition, Paper/ Report/ Annotation, Project
- Weight
- 40%
- Due date
10/11/2025 2:00 pm
Task description
Project Guidelines will be posted on the Blackboard.
The project will require for the student to state the research problem (or a case study) that will need to be solved via analytic (mathematical) formulations, derivations and computations with a software (e.g., Excel QM or other software presented during Lectures or Tutorials). Project report must be presented in a tidy business-style report complemented with two videos (10-minute video and 1-minute video) explaining the work and results obtained by the student.
Note: This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.
Submission guidelines
Electronically via Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0% - 29% |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
A student’s final overall end of semester percentage mark will be rounded to determine their final grade. For example, 64.5% rounds to 65%, while 64.4% rounds to 64%.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Using AI at UQ
Visit the AI Student Hub for essential information on understanding and using Artificial Intelligence in your studies responsibly.
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. You are encouraged to read the UQ Student Integrity and Misconduct Policy available in the Policies and Procedures section of this course profile.
The Academic Integrity Module (AIM) outlines your obligations and responsibilities as a UQ student. It is compulsory for all new to UQ students to complete the AIM.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Other resources, including formula sheets, lecture slides, and solutions toﾠpractical exercises, will be posted on Blackboard.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (28 Jul - 03 Aug) |
Lecture |
Introduction to OR & MS; Optimization and Math Programming Operations Research (OR) & Management Science(MS) approach; constructing & solving OR&MS problems; OR&MS techniques; Break-even Analysis; Intro to Math Programming; Linear Programming; |
Week 2 (04 Aug - 10 Aug) |
Lecture |
Math Programming A the graphical solution method for linear program (LP); characteristics of an LP problem; irregular problems; sensitivity analysis; examples |
Week 3 (11 Aug - 17 Aug) |
Lecture |
Math Programming B Sensitivity analysis; LP in matrix form; the primal and dual LP; duality theorem; integer and 0-1 programs; mixed programs; |
Week 4 (18 Aug - 24 Aug) |
Lecture |
Transportation Problems; Intro to Projects Transportation and assignment problems. Key elements of project management; Gantt charts; CPM & PERT; |
Week 5 (25 Aug - 31 Aug) |
Lecture |
Project Scheduling and Control Project Scheduling and Control (Lecture): the project network; solution algorithms; Probability analysis of a project network; project crashing using CPM; estimating probabilities using PERT. |
Week 6 (01 Sep - 07 Sep) |
Lecture |
Decision Theory A terminology and notation of decision theory; the decision-making process and its components; criteria for decision making; decision-making with and without probabilities; |
Week 7 (08 Sep - 14 Sep) |
Lecture |
Decision Analysis B Bayesian analysis for accounting additional information; utility, different types of risk; basics of game theory |
Week 8 (15 Sep - 21 Sep) |
Lecture |
Inventory Models inventory terminology; notation; the basic EOQM; extensions to the basic EOQM. |
Week 9 (22 Sep - 28 Sep) |
Lecture |
Queueing Theory modelling of the arrival process; the waiting line; modelling of the service process; transient and steady states; M/M/1 systems; M/M/k systems; other systems; |
Mid Sem break (29 Sep - 05 Oct) |
No student involvement (Breaks, information) |
Mid-Sem Break No lecture or tutorial this week. |
Week 10 (06 Oct - 12 Oct) |
No student involvement (Breaks, information) |
Monday - King's Birthday public holiday Students who would normally attend tutorial on this day are advised to attend another session of the week, for this week only. |
Week 11 (13 Oct - 19 Oct) |
Lecture |
Production Analysis & Decisions A Production Theory: profit, revenue, cost; profit efficiency, revenue efficiency, cost efficiency; various technical and allocative efficiency measures; Linear and integer programming problems of production: Data Envelopment Analysis and Free Disposal Hull Analysis; |
Week 12 (20 Oct - 26 Oct) |
Lecture |
Production Analysis & Decisions B Productivity Indexes: Theory and Practice; Estimating productivity indexes with DEA & FDH and via the index numbers approach. |
Week 13 (27 Oct - 02 Nov) |
Lecture |
Review Review of Lecture 1-12 and tips about the Project |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.