Allgemeine Informationen
| Untertitel | |
| Veranstaltungsnummer | 39734 |
| Semester | SoSe 22 |
| Aktuelle Anzahl der Teilnehmenden | 180 |
| Heimat-Einrichtung | Lehrstuhl für Betriebswirtschaftslehre mit Schwerpunkt Management Science / Operations and Supply Chain Management |
| Veranstaltungstyp | Vorlesung in der Kategorie Lehre (mit Prüfung) |
| Erster Termin | Mo., 25.04.2022 12:00 - 14:00 Uhr, Ort: (AM) HS 9 |
| Art/Form | |
| Teilnehmende |
M.Sc.BA, M.Sc.WI, M.Sc.CMT, M.Sc.INF, M.Sc.AIE |
| Lernorganisation |
• Block course with interactive elements and classroom discussions; • Solution and discussions of exercises and case studies; • Online forums and discussions; • A take-home mock exam to simulate the final exam of the course. Discussion of this mock exam; • Blended learning, such as usage of software examples, videos and web-based exercises |
| Leistungsnachweis |
a) Final exam 100 % or b) Final exam 90% + 10 % for completing optional assignments during the semester (with reservations) |
| SWS |
2 |
| Literatur |
Bertsekas, D. P., and Tsitsiklis, J. N. (1996). Neuro-Dynamic Programming. Athena Scientific: Massachustetts. Bertsekas, D. P. Dynamic Programming and Optimal Control. Athena Scientific: Massachustetts. Bertsekas, D. P. Dynamic Programming and Optimal Control: Approximate Dynamic Programming. Athena Scientific: Massachustetts. Bertsekas, D. P. Abstract Dynamic Programming. Athena Scientific: Massachustetts. Powell, W. B. Approximate Dynamic Programming. John Wiley and Sons. |
| Qualifikationsziele |
The main objective of the course is to impart insights into dynamic-programming-based approaches for complex nonlinear optimization problems (especially mixed-integer optimization problems), including online optimization, sequential decision making and stochastic optimization. Students will learn how to approach complexity by incorporating suitable approximation and simulation elements into the design of solution algorithms. The course facilitates critical appreciation of algorithms and algorithmic approaches, including neural networks and reinforcement learning. With help of numerical examples and case studies, the course will prepare students to apply the learned concepts in practice. |
| Workload |
Lecture 2 SWS (30 h attendance and 45h own work) Excercise 2 SWS (30 h attendance and 45 own work) Calculation basis: 15 weeks in a semester, including an examination week; each SWS corresponds to 60 minutes |
| ECTS-Punkte |
5 |