The lectures Heuristic Optimization and Logistics Systems Planning I will not be held in winter term 2022/23 due to a research sabbatical of Prof. Schneider.
There will be no exams in winter term 2022/23.
Despite previous announcements, there will not be a PT3 in the summer term 2022.
Students who take one of the courses in winter term 2021/22 should ideally already register for the PT1 exam so that it is possible to retake PT2 if they do not pass.
Teaching in winter term
The course Heuristic Optimization (lecture + exercise) is delivered following the inverted classroom paradigm. The course is completely online because it is offered within the international collaborations of RWTH. Video recordings of lectures focusing on specific topics are made available here in Moodle. Advanced discussion of the material as well as the chance to ask questions to the lecturer take place in regular lecture online live sessions. The lecture is accompanied by exercise tasks, which students should attempt to solve on their own. Sample solutions will be presented in exercise online live sessions and made available for download later. Questions about both exercises and lectures can also be asked in the forum .
Please note: Recordings and other material will be made available over time and then remain available until the end of the semester. Kick-off and live sessions will not be recorded.
The kick-off will be held on October 12th from 9 :15–10:00.
A detailed schedule and recurring Zoom links for the kick-off and all live sessions will be made available in Moodle.
Complexity theory, greedy algorithm, performance valuation, local search, metaheuristic optimization methods, single-solution methods, population based methods, applying metaheuristic methods for logistic problems, parameter tuning
Operations Research 1 or similar knowledge helpful
100 % exam
understanding the fundamental concepts for the development of good performing metaheuristics
to understand, apply and adopt the most important metaheuristics (tabu search, variable neighborhood search, genetic algorithms, …) to solve logistic problems
to conduct appropriate experiments for fine-tuning the parameters of metaheuristics and to evaluate the performance of metaheuristics