Course Details

Course Information Package

Course Unit TitleDATA MINING
Course Unit CodeMEM517
Course Unit DetailsMSc Engineering Management (Electives Courses) -
Number of ECTS credits allocated7
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
  1. Discuss challenges faced in real, large scale optimization problems in engineering management, including issues of tractability, stochasticity and multi-objectiveness
  2. Explain and critically discuss the available technologies for addressing hard optimization problems such as Mathematical Programming, local search, meta-heuristics and evolutionary algorithms
  3. Analyze engineering systems, formulate optimization problems that correspond to them and design effective solution models for addressing them
  4. Employ the use of advanced optimization tools to solve optimization problems
Mode of DeliveryFace-to-face
Recommended optional program componentsNONE
Course Contents Part A – Algorithms
Multi-objective Optimization
        -  Principles of multi objective optimization: Pareto optimal solutions, objectives in multiobjective optimization, non-conflicting objectives
        -  Difference with single objective optimization
        -  Dominance and pareto optimality
        -  Classical methods: Goal programming, Min-max programming
        -  Local Search Heuristics and Meta-heuristics
        -  Branch and bound Techniques. Beam Search
        -  Neighborhood structures and local search. Local optima entrapment
        -  Single point meta-heuristics: GRASP, Simulated Annealing, Tabu Search, Variable Neighbourhood Search
Evolutionary Algorithms in Optimisation
        -  General structure of a genetic algorithm
        -  Implementation of genetic algorithms
        -  Dominant EA models (NSGA-II, MOEA/D)
Advanced Mathematical Programming
        -  MILP models and modelling approaches
        -  Transformations of non-linear to MILP systems
        -  Stochastic Programming
Part B – Problem applications
A series of application areas known to pose hard optimization problems will be investigated and described algorithms will be used to provide solutions. Case studies from local industries will also be used. The applications investigated will largely depend on the specializations and interest of the cohort; examples of systems examined are provided below:
        -  Plant Scheduling systems.
        -  Resource allocation systems
        -  Vehicle Routing and Transportation problems
        -  Optimization of telecommunications networks
        -  Optimization of water reservoir systems

Recommended and/or required reading:
  • Pardalos P, Resende M Handbook of Applied Optimization, Oxford Press, 2002
  • Various research papers
Planned learning activities and teaching methods The course is delivered through three hours of lectures per week, which include presentation of new material and demonstration of concepts and methods. Lectures also include in-class exercises to enhance the material learning process and to assess the student level of understanding and provide feedback accordingly.
Practical demonstrations and labwork are conducted in computer laboratories using various software specialised optimization tools (e.g. AIMMS modelling package, GANET multi-objective genetic algorithm package).
The course material (notes, exercises, forum, etc) is maintained on the university’s e-learning platform

Assessment methods and criteria
Final Exam25%
Language of instructionEnglish
Work placement(s)NO

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