Postgraduate Course: Advanced Stochastic Optimisation (CMSE11637)
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 | Advanced Stochastic optimisation delves into state-of-the-art quantitative modelling and solution methods for multistage decision-making problems under uncertainty. |
| Course description |
This course aims to broaden and deepen the understanding of uncertainty incorporation in decision-making problems. In many decision-making problems, uncertainty revelation does not happen all at once, but rather in a gradual way. This creates a different dynamic when it comes to optimising and adapting decisions to uncertainty revelation. This course discusses how to optimise decisions in cases where uncertainty revelation happens gradually. The course will equip students with tools to conceptualise decision-making problems that can be adapted to 'time-stamped' data and translate different decision-making philosophies within this context. It will also teach students how to implement and solve these decision-making problems.
The course will cover a subset of the following topics: multistage stochastic programming, decision rule modeling, stochastic dynamic programming, Markov decision processes and decision analysis.
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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Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
|
| 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.
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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 | Multistage stochastic programming,optimisation under uncertainty,mathematical programming |
Contacts
| Course organiser | Dr Aakil Caunhye
Tel:
Email: Aakil.Caunhye@ed.ac.uk |
Course secretary | |
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