IB411-15 Decision Making under Uncertainty
Introductory description
Uncertainty plays a significant role in a wide range of real-world applications, especially in science and engineering, such as manufacturing, transportation, telecommunication, finance, agriculture, and energy etc. Uncertainty modelling is therefore an important issue in solving real-life optimisation problems with data uncertainty.
Stochastic programming is concerned with optimal decision making under uncertainty. The module focuses on several aspects of uncertainty modelling and stochastic programming. In general, the module provides an introduction to models, basic theory and computational methods for optimisation problems under uncertainty as well as a broad overview of its applications in various sectors. It basically focuses on modelling and solving methods as well as real life applications including power generation, financial planning, network resource utilisation, supply chain management, revenue and inventory management.
Module aims
In particular, the module aims are:
-To identify and incorporate uncertainty arising in various industries into the optimal decision making process (stochastic programming models),
-To provide students an introduction to main methodologies for modelling and solving real-life problems under uncertainty,
-To acquaint students with the relevant theory and key techniques for robust and stochastic decision making,
-To make students aware of the capabilities and complexities of different uncertainty models and solution techniques for stochastic programming problems and be able to apply them for various applications,
-To provide students with a hands-on experience of the subject using various case studies and allow them to review the main methodologies and analyse the impact of incorporating uncertainty into the decision making in different applications, and
-To introduce students real life applications and implement the main methodologies to model complex decision making problems.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
The content of module includes the following topics:
- introduction: decision making under uncertainty and its application areas,
- uncertainty modelling,
- two-stage stochastic optimisation models,
- computational software (AMPL),
- computational and practical aspects of two-stage models and their applications,
- robust optimisation,
- chance-constrained models,
- multi-stage modelling and scenario based approaches, and
- real-life applications in multi-stage stochastic programming.
Learning outcomes
By the end of the module, students should be able to:
- Identify key theoretical concepts and methods used for robust and optimal decision making under uncertainty.
- Understand the capabilities and limitations of stochastic models.
- Develop modelling and solving skills for stochastic optimization problems.
- Understand a range of optimization techniques to model and solve stochastic programming problems arising in wide applications.
- Be aware of complex decision making problems and advanced modelling approaches.
Indicative reading list
A. J. King and S. W. Wallace. Modeling with Stochastic Programming. Springer, 2012.
A. Ben-Tal, L. El Ghaoui, and A. Nemirovski. Robust Optimization. Princeton University Press, Princeton, NJ, 2009.
A. Shapiro, D. Dentcheva, and A., Ruszczynski, Lectures on Stochastic Programming: Modeling and Theory. SIAM, Philadelphia, 2009. Ebook Downloadable: http://www2.isye.gatech.edu/people/faculty/Alex_Shapiro/SPbook.pdf
P. Kall and J. Mayer. Stochastic Linear Programming: Models, Theory, and Computation. International Series in Operations Research & Management Science, Vol. 156, Springer, 2011, Edition 2.
S. W. Wallace and W. T. Ziemba (Eds.), Applications of Stochastic Programming, MPS-SIAM Book Series on Optimization 5, 2005.
A. Ruszczynski and A. Shapiro (Eds.), Stochastic Programming, Handbooks in Operations Research and Management Science, Vol. 10, Elsevier, 2003.
Subject specific skills
Model and solve stochastic optimisation model using AMPL.
Learn complex decision-making problems.
Transferable skills
Identify and incorporate uncertainty into decision making process.
Recognise and formulate a practical problem as tochastic program.
Be able to solve the stochastic model using an appropriate method.
Study time
| Type | Required |
|---|---|
| Lectures | 20 sessions of 1 hour (26%) |
| Seminars | 10 sessions of 1 hour (13%) |
| Private study | 48 hours (62%) |
| Total | 78 hours |
Private study description
Private Study.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Individual Assignment (15 CATS) | 20% | 14 hours | Yes (extension) |
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Individual Assignment |
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Reassessment component is the same |
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Assessment component |
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| Quiz work (15 CATS) | 10% | 7 hours | No |
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Quiz work (the scores from the nine weekly quizzes would be added up and the percentage of the whole would be given as this score). |
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Reassessment component is the same |
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Assessment component |
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| Online Examination | 70% | 51 hours | No |
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Exam ~Platforms - AEP |
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Reassessment component is the same |
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Feedback on assessment
Feedback via My.WBS.
Pre-requisites
The students are required to have basic knowledge on modelling and solving linear programming problems as well as probability theory and/or statistics. The module builds upon the material covered in the core modules: Mathematical Programming I (IB104) and Mathematical Programming II (IB207).
To take this module, you must have passed:
There is currently no information about the courses for which this module is core or optional.