Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage verschiedene heuristische und hybride Algorithmen zur Lösung schwieriger kombinatorischer Optimierungsprobleme zu verstehen, anzuwenden und für neue Probleme zu adaptieren.

Combinatorial optimization problems arise in many aspects of human activities. Examples concern packing and cutting problems, timetabling problems, and vehicle routing problems. Many of these problems are computationally very difficult to be solved to optimality. Therefore, simple heuristics (such as greedy algorithms) and metaheuristics (such as tabu search, evolutionary algorithms, and simulated annealing) have achieved a lot of attention from the optimization community during the last decades. At the same time, the operations research community has invested considerable efforts into both exact techniques (such as algorithms based on branch & bound) and general purpose solvers that implement these state-of-the-art exact techniques. Example are CPLEX and Gurobi. This course will give an introduction to these topics. Attendees should bring a laptop with a recent Linux operating system (e.g. Ubuntu) and the GNU g++ compiler installed.

Introduction and explanation of methods, discussion of examples, theoretical exercises, hands-on programming exercises, presentation and discussion of solutions.

ECTS Breakdown:

* 20 hours of theory classes (0.8 ECTS)
* 10 hours of reading homework (0.4 ECTS)
* 70 hours work on the practical project: 2.8 ECTS. Approximate division of these 70 hours:
  + Getting familiar with the given problem and the required tasks: 5 hours
  + Design and implementation of the heuristics and metaheuristics: 20 hours
  + Design and implementation of the hybrid metaheuristics: 10 hours
  + Experimental evaluation: 15 hours
  + Writing the report: 20 hours

Programming assignments, discussion of results, and oral exam.

Solid knowledge in programming, algorithms and data structures.