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COMP9057 - Decision Analytics

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Title:Decision Analytics
Long Title:Decision Analytics
Module Code:COMP9057
 
Credits: 5
NFQ Level:Expert
Field of Study: Computer Science
Valid From: Semester 1 - 2018/19 ( September 2018 )
Module Delivered in 2 programme(s)
Module Coordinator: TIM HORGAN
Module Author: Laura Climent
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.
Learning Outcomes
On successful completion of this module the learner will be able to:
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.
Pre-requisite learning
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 CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
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.
No incompatible modules listed
Co-requisite Modules
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.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
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.
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.
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.
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.
Optimisation
Basic properties of Linear Programming problems. Linear Programming formulation and solving methods. Explanation of Integer Programming and Mixed Integer Programming.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project For a given case study, the student would be expected to model a constraint satisfaction and optimisation problem and also implement an algorithm from Constraint Programming. 1,2,3,5 50.0 Week 8
Project For a given case study, the student would be expected to model a constraint satisfaction and optimisation problem, implement the model and solve it with Linear Programming. 1,2,4,5 50.0 Week 13
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 institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Presentation of theory. 2.0 Every Week 2.00
Lab Lab supporting lectures. 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Student undertakes independent study. The student reads recommended papers and practices implementation. 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Presentation of theory. 2.0 Every Week 2.00
Lab Lab supporting lectures. 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Student undertakes independent study. The student reads recommended papers and practices implementation. 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Thomas H. Cormen , Charles E. Leiserson , Ronald L. Rivest , Clifford Stein 2009, Introduction to Algorithms, MIT Press [ISBN: 9780262533058]
Supplementary Book Resources
  • Francesca Rossi 2006, Handbook of Constraint Programming (Foundations of Artificial Intelligence), Elsevier Science [ISBN: 9780444527264]
  • Frederick S. Hillier 2014, Introduction to Operations Research, McGraw-Hill Education [ISBN: 9781259253188]
Recommended Article/Paper Resources
  • Du, Ding-Zhu, and Panos M. Pardalos, eds. 2013, Handbook of combinatorial optimisation: supplement, Springer Science & Business Media, Vol. 1.
Supplementary Article/Paper Resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_KSADE_9 Master of Science in Software Architecture & Design 1 Elective
CR_KSADE_9 Master of Science in Software Architecture & Design 2 Elective

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

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Email: help@cit.edu.ie