Postgraduate Course: Stochastic Optimization (CMSE11500)
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 | 10 |
ECTS Credits | 5 |
| Summary | Stochastic Optimization provides an introduction to state-of-the-art quantitative modelling and solution methods for decision-making problems under uncertainty.
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| 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: stochastic programming, robust optimization, decision rule modeling, stochastic dynamic programming, Markov decision processes and decision analysis.
Student Learning Experience:
Weekly classes and hands-on programming exercises which enable students to implement the methodologies covered in class.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
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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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% coursework (Individual) - assesses all course Learning Outcomes |
| Feedback |
Formative: Students will receive formative feedback during lectures and in individual meetings with Course Organiser.
Summative: Students will receive summative feedback after submission of project reports.
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| 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 optimized decisions and its implications
- Communicate findings effectively to a critical business audience
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Reading List
Indicative 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
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Additional Information
| Graduate Attributes and Skills |
Knowledge and Understanding:
After completing this course, students should be able to:
1. 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.
2. 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.
Communication, ICT and numeracy skills:
After completing this course, students should be able to:
1. 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.
2. 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. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Aakil Caunhye
Tel:
Email: Aakil.Caunhye@ed.ac.uk |
Course secretary | Ms Heather Ferguson
Tel: (0131 6)50 8074
Email: Heather.Ferguson@ed.ac.uk |
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