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| Module Availability |
Autumn Semester |
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| Assessment Pattern |
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Components of Assessment
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Method(s)
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Weighting
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Examination
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Written 2 hour examination (closed book) |
70%
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| Coursework |
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30%
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Qualifying Condition(s)
A weighted aggregate mark of 50% is required to pass the module. |
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| Module Overview |
| Systems engineering is best demonstrated when supporting decisions for the design and the operation of large and complex problems. Support may relate to strategic, tactical and day-to-day decisions covering a wide range of applications in process engineering, scheduling, planning and task management. The module presents generic technologies with an emphasis on model-based optimization (mathematical and stochastic programming, discrete optimization), presenting selected applications in process synthesis, operations management and technologies affecting the development of modern supply chains. |
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| Prerequisites/Co-requisites |
None |
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| Module Aims |
The course seeks to develop students’ understanding of the technology available to optimise business and engineering systems using a modelling approach. The course addresses design and operational problems explaining principles and available platforms to use. Students are provided with state-of-the-art approaches to understand the potential as well as known limitation of the available techniques. They learn how to associate decision-making with quantitative techniques and how to use quantitative methods to make decisions for large and complex problems.
The primal aim of the module is to teach students how to formulate decision-making problems and how to apply the technology to support decisions. The module includes hands-on sessions with the General Algebraic Modelling System (GAMS) and coursework to support the lectures. |
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| Learning Outcomes |
On successful completion of the module, you will be able to:
· Formulate optimization and decision-support models;
· Identify the optimisation technology appropriate for a particular problem;
Use commercial modelling platforms (GAMS) to solve small and large problems |
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| Module Content |
Introduction;
Optimisation models;
Algebraic modelling systems;
Basic of optimisation theory;
Sensitivity analysis and multi-objective optimisation;
Commercial solvers and web-enabled optimisation;
Synthesis, discrete optimisation and mixed-integer programming;
Optimisation and decision-making in Operations Management |
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| Methods of Teaching/Learning |
The teaching and learning methods include lectures, class discussions, working sessions and assignments.
Total student learning time is 150 hours. |
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| Selected Texts/Journals |
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None
Required reading
None
Recommended background reading
Floudas C, Nonlinear and Mixed-Integer Optimization, Oxford, 2004
Biegler LT, Grossmann IE, and Westerberg AW, Systematic Methods in Chemical Process Design, Prentice Hall, 1997. |
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| Last Updated |
02/10/2009 |
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