Universität Wien

390024 DK PhD-BALOR: Advanced Methods of Business Analytics (2024S)

5.00 ECTS (2.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 24 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Dienstag 21.05. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 21.05. 13:15 - 16:30 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Mittwoch 22.05. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Mittwoch 22.05. 13:15 - 16:30 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 23.05. 09:45 - 11:15 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 23.05. 11:30 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 23.05. 13:15 - 16:30 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Freitag 24.05. 09:40 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung


4. Course Description
5.1 Abstract and Learning Objectives
The 4-day course deals with anticipatory methods for dynamic decision making. It will address the following questions:
1. What are the components of dynamic decision processes and how do they interact?
2. How can dynamic decision processes be modeled mathematically?
3. What methods exist in approximate dynamic programming?
4. How can they be applied to different types of problems?

Art der Leistungskontrolle und erlaubte Hilfsmittel

Students are required to read the mandatory literature. In addition, students should summarize their research topic in 3 to 5 slides. If applicable, they should highlight potential uncertainty/stochasticity in their topic.

Mindestanforderungen und Beurteilungsmaßstab

Prüfungsstoff

6.1 Assignments
The students will work on their selected problems at the end of every day. They will present their results at the beginning of the following day.
Every student will write a succinct summary of the developed models and methods for the individual case studies (about 6-8 pages).
The final grade will be based on class participation during the lectures and case studies (50%) and the quality of the summary to be submitted after the course (50%).

Literatur

5.2 Essential Reading Material
Participants are required to read some overview literature as part of their preparation for the course.
1. Powell, W. B. (2009). What you should know about approximate dynamic programming. Naval Research Logistics (NRL), 56(3), 239-249.
2. Ulmer, M. W., Goodson, J. C., Mattfeld, D. C., & Thomas, B. W. (2020). On modeling stochastic dynamic vehicle routing problems. EURO Journal on Transportation and Logistics, 9(2), 100008
3. Soeffker, Ni, Marlin W.U., and Mattfeld, D.C (2021). Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review." European Journal of Operational Research, 3(1), 801-820.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 31.07.2024 12:06