MXN431 Advanced Operations Research

Unit Outline: Semester 1 2024, Gardens Point, Internal

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:

1. Employ advanced knowledge of optimisation and meta-heuristics necessary for advanced applications in economics, scientific, medical, health, environmental and/or engineering problems.
2. Engage with end-users to understand their optimisation objectives and constraints, and to formulate a model of their system dynamics.
3. Formulate and apply advanced computational optimisation approaches to offer useful solutions to these problems.
4. Demonstrate advanced practical ability to employ optimisation and meta-heuristics.
5. Summarise and explain the application of optimisation and meta-heuristics in written and/or oral form.

Content

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.

For more information regarding expected volume of learning for this unit, please consult QUT Manual of Policies and Procedures, Section C/3.1.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.

For more information regarding expected volume of learning for this unit, please consult QUT Manual of Policies and Procedures, Section C/3.1.

Assessment

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Case Study

This case study will focus on a challenging deterministic decision problem from an industry, logistics or environmental management setting. You will be required to synthesise a realistic optimisation problem in mathematical form, and implement an optimisation technique (approximate or exact) to generate optimal or near-optimal solutions to given problem instances. You will be required to demonstrate through numerical experiments that the solution approach has been appropriately tuned and an estimate of average optimality gap is to be determined.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Problem Solving Task

This task will focus on a decision problem 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.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Academic Integrity

Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.

Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.

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.

Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties 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.