Postgraduate Course: Prescriptive Analytics with Stochastic Programming (CMSE11652)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | 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 optimised 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.
Outline content
The course will cover a subset of the following topics: stochastic programming, robust optimisation, decision rule modeling, stochastic dynamic programming, Markov decision processes and decision analysis.
Student Learning Experience
Weekly lectures and hands-on programming exercises which enable students to implement the methodologies covered in class.
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Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
60% Class test (Individual) - Assesses all course Learning Outcomes«br /»
40% Project report (Group) includes 10% peer review - Assesses all course Learning Outcomes |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on assessments 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
Core text(s)
John R. Birge and François Louveaux, Introduction to Stochastic Programming, Springer, 2011
Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski, Robust Optimisation, Princeton University Press, 2009 |
Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
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
After completing this course, students should be able to:
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,Optimisation under uncertainty,Mathematical programming |
Contacts
| Course organiser | Dr Aakil Caunhye
Tel:
Email: Aakil.Caunhye@ed.ac.uk |
Course secretary | Miss Quinny Jiang
Tel:
Email: Quinny.Jiang@ed.ac.uk |
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