Postgraduate Course: Introduction to Stochastic Optimisation (CMSE11639)
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 | Introduction to Stochastic Optimisation provides an introduction to state-of-the-art quantitative modelling and solution methods for decision-making problems under uncertainty. |
| Course description |
This course aims at introducing students to concepts related to the quantitative modeling of decision-making problems under uncertainty. In almost all decision-making problems, data contain uncertainties that are uncontrollable and that need to be factored in to build systematic, long term and optimized decisions. This course covers various philosophies of approaching uncertainty in decision making, providing different perspectives on how to quantitatively conceive uncertainty and how to use these conceptions of uncertainty in formulating solvable decision-making models. The course also covers various tools to solve and analyse these decision-making problems so as to broaden the understanding of prescriptive analytics and provide more versatility in conceiving, understanding, interpreting and solving decision-making models.
The course will cover a subset of the following topics: two-stage stochastic programming, robust optimisation, decision rule modeling.
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.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Applied Decision Optimisation (CMSE11612)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Optional course for Online MSc in Data and Decision Analytics. |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
86 )
|
| 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:
- Motivate, describe, model and solve decision-making problems under uncertainty.
- Critically appraise the suitability of different modeling techniques and assess the resulting optimised decisions and its implications.
- Communicate findings effectively to a critical business audience.
|
Reading List
John R. Birge and François Louveaux, Introduction to Stochastic Programming, Springer, 2011
Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski, Robust Optimization, Princeton University Press, 2009 |
Additional Information
| Graduate Attributes and Skills |
After completing this course, students should be able to:
Communication, ICT, and Numeracy Skills
-Convey meaning and message through a wide range of communication tools, including digital technology
and social media; to understand how to use these tools to communicate in ways that sustain positive and
responsible relationships.
-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.
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 | Stochastic programming,optimization under uncertainty,mathematical programming |
Contacts
| Course organiser | Dr Aakil Caunhye
Tel:
Email: Aakil.Caunhye@ed.ac.uk |
Course secretary | |
|
|
University Homepage