Universität Wien

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

5.00 ECTS (2.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 24 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 21.05. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 21.05. 13:15 - 16:30 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 22.05. 09:45 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 22.05. 13:15 - 16:30 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 23.05. 09:45 - 11:15 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 23.05. 11:30 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 23.05. 13:15 - 16:30 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 24.05. 09:40 - 13:00 Seminarraum 4 Oskar-Morgenstern-Platz 1 1.Stock

Information

Aims, contents and method of the course


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?

Assessment and permitted materials

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.

Minimum requirements and assessment criteria

Examination topics

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%).

Reading list

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.

Association in the course directory

Last modified: We 31.07.2024 12:06