Postgraduate Course: Heuristic Optimisation (CMSE11638)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Real life decision problems are often too complicated to be modelled by e.g., mathematical tools. Even if they are modelled, these type of problems are often intractable and extremely challenging to solve. In recent years, the emergence of approximation techniques as an alternative way of solving problems in areas such as optimisation has attracted attentions from both academics and practitioners. This course offers alternative approaches to solve complex problems which could otherwise be difficult to solve by traditional techniques. It aims at training students in the field of approximation (e.g., heuristics, metaheuristics, hyperheuristics and evolutionary computations) to address decision making problems in business. |
| Course description |
The goal of this course is to provide the students with sufficient knowledge to understand and implement modern heuristic algorithms to address large-scaled, high-dimensional optimisation problems.
1. Local search algorithms and heuristics
2. Metaheuristics
3. Evolutionary Computation
4. Hyperheuristics
Tutorial/seminar hours represent the minimum total live hours - online - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously. Live sessions will be delivered only once.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | For MSc in Data and Decision Analytics students only. |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Block 1 (Sem 1) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 3,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
85 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% coursework (individual) - assesses all course Learning Outcomes |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on the assessment within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand how optimisation problems could be modelled using approximation techniques.
- Analyse decision problems in business settings using approximation techniques (e.g., heuristics, metaheuristics etc).
- Implement one or more approximation technique(s) by means of a computer programming language, interpret results and formulate managerial guidelines and make recommendations.
- Communicate findings effectively and efficiently verbally and in writing.
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Reading List
| Heuristic Search The Emerging Science of Problem Solving, Palgrave Macmillan; 1st ed. 2017 edition ISBN 10: 331949354X |
Additional Information
| Graduate Attributes and Skills |
After completing this course, students should be able to:
Communication, ICT, and Numeracy Skills
-Critically evaluate and present digital and other sources, research methods, data and information; discern
their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of
organisational contexts.
Practice: Applied Knowledge, Skills and Understanding
-Apply creative, innovative, entrepreneurial, sustainable and responsible business solutions to address
social, economic and environmental global challenges
Cognitive Skills
-Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating
adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to
demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to
quality
Knowledge and Understanding
-Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines;
comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary
and secondary research and sources of evidence in order to make, and present, well informed and transparent
organisation-related decisions, which have a positive global impact.
-Identify, define and analyse theoretical and applied business and management problems, and develop
approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore
and solve them responsibly. |
| Keywords | Heuristics,metaheuristics,hyperheuristics and evolutionary computations |
Contacts
| Course organiser | Dr Nader Azizi
Tel: (0131 6)51 1491
Email: Nader.Azizi@ed.ac.uk |
Course secretary | Mx Fran Knocke
Tel:
Email: Fran.Knocke@ed.ac.uk |
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