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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 - 2017/18 ( September 2017 )
Module Delivered in 2 programme(s)
Module Coordinator: TIM HORGAN
Module Author: Laura Climent
Module Description: In many real-life applications with underlying constraints there are several parameters to configure. Solving such problems involves finding configurations that satisfy the constraints associated to the problem. In addition, many times there are certain criteria to optimise. Many configurations are incompatible and only few are the best (optimal solutions). In this module, students will learn to model optimisation problems and apply algorithms that facilitate decision making leading to optimal solutions.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Assess a wide range of real-life problems from various application areas to which decision analytics can be applied.
LO2 Model optimisation problems as Constraint Satisfaction and Optimisation Problems, by identifying their main characteristics.
LO3 Evaluate an algorithm to determine its soundness and completeness for a particular optimisation problem.
LO4 Select and apply an appropriate algorithm for a given optimisation problem.
LO5 Solve several real-world problems using both complete and incomplete algorithms, comparing the quality of the solutions and their computation time.
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 optimisation real-life problems and motivation to the importance of the application of decision analytics to them in order to try 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. Explanation of Branch & Bound algorithm for finding optimal solutions. Brief mention to Boolean Satisfiability.
Soundness and Completeness
Explanation of the concepts soundness and completeness associated to the algorithms by providing several examples of algorithms of each type. Explanation of the benefits and disadvantages of complete/incomplete algorithms and the more appropriate scenarios of applicability for each type.
Mathematical Optimisation
Basic properties of Linear Programming problems. Linear Programming formulation and solving methods such as Simplex algorithm. Explanation of Integer Programming and Mixed Integer Programming.
Meta-heuristics
Heuristic definition and explanation of the properties of meta-heuristic algorithms. Brief introduction to the types of meta-heuristic algorithms and their classification: local search, simulated annealing, and some evolutionary algorithms such as genetic algorithms.
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 use the most appropriate algorithm for solving it. Also indicate the soundness and completeness of the algorithm. 1,2,3,4,5 65.0 Week 9
Project The student would be asked to solve certain questions of the design of a given meta-heuristic. For instance, evaluate the fitness of some candidate solutions, perform crossover operations (in case of a genetic algorithm), etc. Also, indicate the soundness and completeness of the algorithm. 1,3,5 35.0 Sem End
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) Independent student learning and study. 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) Independent student learning and study. 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, Introduction to Algorithms [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]
  • Karl F. Doerner 2010, Metaheuristics: Progress in Complex Systems Optimization, Springer [ISBN: 9781441944214]
  • Holger H. Hoos 2014, Stochastic Local Search: Foundations & Applications, Morgan Kaufmann [ISBN: 9781493303731]
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
  • Glover, Fred W., and Gary A. Kochenberger, eds. 2006, Handbook of metaheuristics, Springer Science & Business Media, Vol. 57.
  • 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, 49-78
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
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