040186 KU Transportation Logistics (MA) (2018S)
Prüfungsimmanente Lehrveranstaltung
Labels
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.
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mi 14.02.2018 09:00 bis Mi 21.02.2018 12:00
- Abmeldung bis Mi 14.03.2018 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Mittwoch
07.03.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
14.03.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
21.03.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
11.04.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
18.04.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
25.04.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
02.05.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
09.05.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
16.05.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
23.05.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Montag
28.05.
15:00 - 16:30
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
30.05.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
13.06.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Freitag
22.06.
13:15 - 14:45
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
Mittwoch
27.06.
11:30 - 13:00
Hörsaal 7 Oskar-Morgenstern-Platz 1 1.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.
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
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: Mo 07.09.2020 15:28