186.112 Heuristic Optimization Techniques
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020W, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to understand diverse heuristic algorithms for solving hard optimization problems, to apply them in practice, and to adapt them to new problems.

Subject of course

This lecture deals with heuristic methods to solve optimization problems. The presented approaches are especially suitable for problems arising in practice. On the one hand such problems are often too complex to be solved in an exact way because of the increasing amount of computation time needed by conventional exact techniques. On the other hand it is often sufficient or even required to come up with a good solution in reasonable time.

In this course we primarily focus on discrete appilcation problems and application in areas such as transport optimization, scheduling, network design, cutting and packing.

The methods considered in the course include:

  • Construction Heuristics
  • Local Search
  • Simulated Annealing
  • Tabu-Search
  • Guided Local Search
  • Variable Neighborhood Search
  • Very Large Neighborhood Search
  • Greedy Randomized Adaptive Search Procedure
  • Genetic Algorithms
  • Evolutionary Strategies
  • Ant Colony Optimization
  • Hybridization of different approaches, parallelization
  • Analysis and Tuning of metaheuristics

Beside the theoretical basics this lecture focuses on practical applications and the connection of metaheuristics with problem-specific heuristics as well as some examples of suitable combinations with exact methods.

Also we will discuss how to properly tune heuristics and to evaluate and compare them by means of experiments and appropriate statistical methods.

Teaching methods

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

Mode of examination


Additional information


20h Lectures
  5h Recap lecture contents
40h Exercises
  8h Exam preparation
  2h Exercise interviews / Examination

Hotline for any questions concerning this course: heuopt (at) ac.tuwien.ac.at



Course dates

Tue11:15 - 12:4506.10.2020 - 19.01.2021 ZoomLecture
Wed11:15 - 12:4509.12.2020 ZoomLecture
Heuristic Optimization Techniques - Single appointments
Tue06.10.202011:15 - 12:45 ZoomLecture
Tue13.10.202011:15 - 12:45 ZoomLecture
Tue20.10.202011:15 - 12:45 ZoomLecture
Tue27.10.202011:15 - 12:45 ZoomLecture
Tue03.11.202011:15 - 12:45 ZoomLecture
Tue10.11.202011:15 - 12:45 ZoomLecture
Tue17.11.202011:15 - 12:45 ZoomLecture
Tue24.11.202011:15 - 12:45 ZoomLecture
Tue01.12.202011:15 - 12:45 ZoomLecture
Wed09.12.202011:15 - 12:45 ZoomLecture
Tue15.12.202011:15 - 12:45 ZoomLecture
Tue12.01.202111:15 - 12:45 ZoomLecture
Tue19.01.202111:15 - 12:45 ZoomLecture

Examination modalities

Assignments / final oral exam

During the course two assignments have to be solved and handed in. Each assignment consists of a theoretical exercise part and a programming exercise for which concise reports have to be prepared. The programming exercise parts are meant to be solved in teams of two students. Each team will present their solutions in two interviews.

To complete the course it is mandatory to solve and hand in the solutions of the assignments. The second interview is in connection with an oral examination about the course topics. The assignments and the oral exam each contribute one half to the final grade and each of them has to be positive to successfully complete the lecture.

First Interview session in the week beginning with 7th of December via online meeting.
Second Interview session including exam in the week beginning with 18th of January via online meeting.

Course registration

Begin End Deregistration end
15.09.2020 00:00 20.10.2020 23:55 20.10.2020 23:55



  • F. Glover, G. A. Kochenberger: Handbook of Metaheuristics, Kluwer Academic Publishers, 2003
    (comprehensive, recent standard work on metaheuristics)
  • M. Gendreau, J.-Y. Potvin: Handbook of Metaheuristics, 2nd edition, Springer, 2010
    (describes various methods in addition to the first version)
  • E. Talbi: Metaheuristics: From Design to Implementation, J. Wiley and Sons, 2009
    (new and detailed work about metaheuristics)

Previous knowledge

Basic knowledge in algorithms and data structures, programming skills

Preceding courses

Accompanying courses

Continuative courses