Module overview
The overreaching goal of this module is to develop your quantitative problem solving skills by improving your algorithmic thinking and computer implementation skills. The module aims to provide you with in-depth knowledge about the contemporary optimisation methods, their strengths, efficient implementations and application areas, usability and shortcomings. The module emphasises the versatility of the methods, and encourages you to apply these techniques to diverse areas of business, in order to reorient your thinking processes towards a perspective of continuous improvement of every process.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- modelling and solving decision problems.
- the state-of-the-art optimisation methods;
- the appropriate optimisation method to solve a business problem;
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- combine and enhance existing methods to achieve better performance;
- critically evaluate business processes for areas of improvement.
- design and improve your own methods;
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- apply quantitative decision making methods
- structure and solve problems
- utilise written communication.
Syllabus
No prior knowledge on optimisation or programming is required, although recommended. The topics to be
covered include, but are not limited to
- Basics of optimisation: what is a problem, what is an algorithm, introduction to writing computer programs, algorithm analysis and time complexity, introduction to optimisation software;
- Standard problems in logistics and their applications: including knapsack and bin packing problems, cutting stock problems, facility location and clustering problems, inventory and lot sizing problems, network flow problems; -Methods of optimisation: optimal and heuristic approaches, use of optimisation software, programming your own algorithms;
- Case study on vehicle routing algorithms: constructive heuristics, approximation algorithms, local search, metaheuristics and population based heuristics;
- Case studies in organisation of logistics, wider context and issues.
Learning and Teaching
Teaching and learning methods
Learning activities include:
- Lectures
- Use of online materials
- An individual assignment
- In-class case study / problem solving activities
- Private study.
| Type | Hours |
|---|---|
| Independent Study | 126 |
| Teaching | 24 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Talbi, E., Metaheuristics (2009). From Design to Implementation. New Jersey: Wiley.
Michalewicz, Z. and Fogel, D. B (2000). How to Solve it: Modern Heuristics. Berlin: Springer Verlag.
Winston, W (2004). Operations Research: Applications and Algorithms. Brooks/Cole,.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Feedback
- Assessment Type: Formative
- Feedback: Individual help is given verbally during the lectures; one-on-one meetings with individual students outside the module; individual feedback is provided during the code and report (before submission).
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Report | 50% |
| Exam | 50% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |