Module Details
| Module Code: |
COMP9057 |
| Title: |
Decision Analytics
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Long Title:
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Decision Analytics
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| NFQ Level: |
Expert |
| Valid From: |
Semester 1 - 2018/19 ( September 2018 ) |
| Field of Study: |
4811 - Computer Science
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| Module Description: |
Many real-world problems require the optimization of an objective function while satisfying underlying constraints. In this module, students will learn how to model real-world optimization problems. They will also learn to select and apply appropriate optimization algorithms, which find optimal solutions via decision making.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Assess a wide range of real-world problems, which can be solved through the application of decision analytics. |
| LO2 |
Model a variety of real-world problems, identifying their main characteristics as constraint satisfaction and optimisation problems. |
| LO3 |
Apply constraint programming algorithms to solve real-world problems. |
| LO4 |
Apply linear programming algorithms to solve real-world optimisation problems. |
| LO5 |
Evaluate an algorithm to determine its soundness and completeness for a particular optimisation problem. |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
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| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
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| No requirements listed |
| Indicative Content |
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Introduction
Introduction to real-life optimisation problems. Overview of the application of decision analytics in order to obtain the best solution according to certain criterion/criteria such as minimising costs, maximising benefits, etc. Explanation of the features of combinatorial optimisation.
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Applications to Real-life Problems
Analysis and solving of combinatorial optimization problems such as vehicle routing problem, scheduling problems, cutting stock problem, bin packing problem, etc.
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Modelling & Constraint Programming
Modelling real-life problems as Constraint Satisfaction Problems. Explanation of Constraint Propagation techniques such as: node-consistency, arc-consistency and path-consistency. As well as the explanation of backtracking algorithms and algorithms that combine search and constraint propagation such as: Maintaining Arc Consistency (MAC). Extension of the Constraint Satisfaction Problems to Constraint Satisfaction and Optimisation Problems. Branch & Bound algorithm for identifying optimal solutions and Boolean Satisfiability.
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Soundness and Completeness
Explanation of the concepts of soundness and completeness by providing several examples of algorithms of each type. Explanation of the advantages and disadvantages of complete/incomplete algorithms and the more appropriate scenarios of applicability for each type.
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Optimisation
Basic properties of Linear Programming problems. Linear Programming formulation and solving methods. Explanation of Integer Programming and Mixed Integer Programming.
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Module Content & Assessment
|
| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
Assessments
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
|
The University reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Presentation of theory. |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Lab supporting lectures. |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Student undertakes independent study. The student reads recommended papers and practices implementation. |
Every Week |
3.00 |
3 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Presentation of theory. |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Lab supporting lectures. |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Student undertakes independent study. The student reads recommended papers and practices implementation. |
Every Week |
3.00 |
3 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
|
Module Resources
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| Recommended Book Resources |
|---|
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Thomas H. Cormen , Charles E. Leiserson , Ronald L. Rivest , Clifford Stein. (2009), Introduction to Algorithms, MIT Press, [ISBN: 9780262533058].
| | Supplementary Book Resources |
|---|
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Francesca Rossi. (2006), Handbook of Constraint Programming (Foundations of Artificial Intelligence), Elsevier Science, p.978, [ISBN: 9780444527264].
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Frederick S. Hillier. (2014), Introduction to Operations Research, McGraw-Hill Education, p.1088, [ISBN: 9781259253188].
| | Recommended Article/Paper Resources |
|---|
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Du, Ding-Zhu, and Panos M. Pardalos,
eds.. (2013), Handbook of combinatorial optimisation:
supplement, Springer Science & Business Media, Vol. 1..
| | Supplementary Article/Paper Resources |
|---|
-
Laura Climent, Richard J. Wallace,
Miguel A. Salido, Federico Barber. (2014), Robustness and Stability in Constraint
Programming under Dynamism and
Uncertainty, Journal of Artificial Intelligence
Research, 49, p.49-78,
| | Other Resources |
|---|
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Website, Global Optimization Test Problems,
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Website, Discrete Optimization Coursera,
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Website, IBM ILOG CPLEX Optimization Studio,
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