Universität Wien

040186 KU Transportation Logistics (MA) (2024S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung

The course language is English.

Only students who signed up for the class in univis/u:space are allowed to take the class (that means, that you have to at least be on the waiting list if you want to take this class). No exceptions possible.
Moodle

An/Abmeldung

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

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Dienstag 05.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 19.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 09.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 16.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 23.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 30.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 07.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 14.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 21.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 28.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 04.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 11.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 18.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 25.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course is an introduction to some optimisation problems that appear in Transportation Logistics. Along the semester, we will cover several of these problems, and study their corresponding mathematical formulations and solution methods.

Among the problems we will study: classical network problems (minimum spanning tree, shortest paths, maximum flows), warehouse location problem and its capacitated version, transportation problem, assignment problem, knapsack problem, orienteering, traveling salesman and vehicle routing problems, and maybe others.

Among the methods we will learn: combinatorial algorithms, modelling and solving network problems as linear programs, simplex, dynamic programming, and (for the harder problems), branch-and-bound. For some of the hard problems, we will also discuss construction and improvement heuristics.

This course is broad rather than deep, which means that we emphasise covering a good number of problems and methods, without spending too much time in any of them. The focus is on learning methods, and developing intuition behind why they work.

Art der Leistungskontrolle und erlaubte Hilfsmittel

2 exams, each corresponding to 40% of the final grade.
Several homeworks to be announced every week, and handed in in the beginning of the following class. These account for the remaining 20% of the grade.

Mindestanforderungen und Beurteilungsmaßstab

Students should be familiar with Excel (in special, the Solver), and have basic knowledge about linear programming (i.e., understand a LP formulation, and how to apply the simplex method).

This course requires a somewhat higher level of abstraction, when compared to a Bachelor course. Students are expected to spend around 1-2 hours per week in out-of-class studies (reviewing the content, and preparing the homeworks).

Prüfungsstoff

Slides will be available through Moodle, and are sufficient for covering all the content of the course.

Literatur

Slides will be available through Moodle, and are sufficient for covering all the content of the course.

For a quick review of linear programming, including the simplex method, students are referred to:

Hillier, Lieberman. Introduction to Operations Research. Chapters 1-5.

(Optional!) For a deeper and more rigorous understanding of many of the methods we see in this course, students are referred to:

Bertsimas, D., Tsitsiklis, J. Introduction to Linear Optimization.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 31.07.2024 11:25