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The fundamental goal of this course is to provide a framework to solve optimization problems with discrete or integer variables. The course aims to teach the modeling, relaxing and bounding techniques. The topics will also include cutting plane algorithms, heuristics and approximation.
Modeling, relaxing and bounding techniques. Fundamental easy-to-solve problems. Branch-and-bound, cutting planes method, branch and price and column generation methods. Dynamic programming. Meta-heuristics such as tabu search, genetic algorithms and variable neighborhood search. Application examples.
Upon succesful completion of this course, a student will be able to
1. Model optimization problems with discrete or integer variables (c),
2. Use relaxing and bounding techniques in discrete models (c),
3. Apply heuristic methods and approximation algorithms to find good solutions to integer programming models (h),
4. Use the branch and bound algorithm and the cutting plane algorithm for solving integer programming problems (i).