Module Title - Heuristic Optimization
Number of credits – 10
Most problems from management mathematics (discrete or continuous) are NP-hard. In other words, optimization problems that arise in industry or in public sector could not be solved exactly in reasonable computing time, even with modern computers.
Therefore, when traditional mathematics techniques fail to give fast answer, one should rely on near-optimal solution methods or heuristics. Ideas of classical heuristics (greedy, constructive, relaxation, local search, Lagrangean, etc.) will be studied first.
A modern heuristics (metaheuristics) or general frameworks for building heuristics, usually give rules of escaping from the so-called “local optima trap”. Such methods are Genetic algorithms, Tabu search, Variable neighbourhood search, etc.
By the end of the module you will be able to:
- understand and explain why and when heuristic optimization techniques are useful in Management mathematics;
- understand and explain the basic concepts of classical heuristic optimization techniques;
- design data structure for the computer code and apply rules of heuristics for that problem;
- By the end of the module students should be able to explore these topics beyond the taught syllabus.
Teaching and assessment:
- Assessment: 10% Coursework, 90% Examination.
- Semester 2.
- Teaching: 22 hours of lectures, 5 hours examples classes.