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, 2024 (22/07/2024 - 18/11/2024)
- 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 14 August (Royal Queensland Show), Mon 7 October (King's Birthday).
In-Semester Break: 23 - 29 September. Semester 2 classes recommence Mon 30 September.
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. The timetable can be downloaded hereﾠPublic Timetable.
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, Paper/ Report/ Annotation, Project | Business Improvement Research Project | 25% |
4/11/2024 2:00 pm |
| Computer Code, Tutorial/ Problem Set | Assignment 1 | 25% |
16/08/2024 1:00 pm |
| Computer Code, Tutorial/ Problem Set | Assignment 2 | 25% |
13/09/2024 1:00 pm |
| Computer Code, Tutorial/ Problem Set | Assignment 3 | 25% |
11/10/2024 1:00 pm |
Assessment details
Business Improvement Research Project
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation, Project
- Weight
- 25%
- Due date
4/11/2024 2:00 pm
Task description
Project Guidelines will be posted on the Blackboard.
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.
Assignment 1
- Mode
- Written
- Category
- Computer Code, Tutorial/ Problem Set
- Weight
- 25%
- Due date
16/08/2024 1:00 pm
Task description
Problems (or case studies) that will generally need to be solved via analytic (mathematical) formulations, derivations and computations. Some problems may require using Excel QM or other software presented during Lectures or Tutorials.
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.
Assignment 2
- Mode
- Written
- Category
- Computer Code, Tutorial/ Problem Set
- Weight
- 25%
- Due date
13/09/2024 1:00 pm
Task description
Problems (or case studies) that will generally need to be solved via analytic (mathematical) formulations, derivations and computations. Some problems may require using Excel QM or other software presented during Lectures or Tutorials.
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.
Assignment 3
- Mode
- Written
- Category
- Computer Code, Tutorial/ Problem Set
- Weight
- 25%
- Due date
11/10/2024 1:00 pm
Task description
Problems (or case studies) that will generally need to be solved via analytic (mathematical) formulations, derivations and computations. Some problems may require using Excel QM or other software presented during Lectures or Tutorials.
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
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. Further information on plagiarism and how to avoid an allegation of plagiarism is available in this course profile under Policies & Guidelines. Please refer to the Academic Integrity Module (AIM). It is strongly recommended that you complete the AIM if you have not already done so.
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 (22 Jul - 28 Jul) |
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 (29 Jul - 04 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 (05 Aug - 11 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 (12 Aug - 18 Aug) |
No student involvement (Breaks, information) |
Wednesday - Royal Queensland Show public holiday Students who would normally attend tutorial on this day are advised to attend another session of the week, for this week only. |
Lecture |
Transportation Problems; Intro to Projects Transportation and assignment problems. Key elements of project management; Gantt charts; CPM & PERT; |
|
Week 5 (19 Aug - 25 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 (26 Aug - 01 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 (02 Sep - 08 Sep) |
Lecture |
Decision Analysis B Bayesian analysis for accounting additional information; utility, different types of risk; basics of game theory |
Week 8 (09 Sep - 15 Sep) |
Lecture |
Inventory Models inventory terminology; notation; the basic EOQM; extensions to the basic EOQM. |
Week 9 (16 Sep - 22 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 (23 Sep - 29 Sep) |
No student involvement (Breaks, information) |
Mid-Sem Break No lecture or tutorial this week. |
Week 10 (30 Sep - 06 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 11 (07 Oct - 13 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 12 (14 Oct - 20 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 (21 Oct - 27 Oct) |
Lecture |
Review |
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 - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.