# 186.190 Optimization in Transport and Logistics This course is in all assigned curricula part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_21",{id:"j_id_21",showEffect:"fade",hideEffect:"fade",target:"isAllSteop"});});This course is in at least 1 assigned curriculum part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_23",{id:"j_id_23",showEffect:"fade",hideEffect:"fade",target:"isAnySteop"});}); 2024S 2018S 2017S 2016S 2015S 2014S 2013S 2012S 2011S 2010S 2009S

2024S, VU, 2.0h, 3.0EC

## Properties

• Semester hours: 2.0
• Credits: 3.0
• Type: VU Lecture and Exercise
• Format: Presence

## Learning outcomes

After successful completion of the course, students are able to understand and apply the basics of modeling, algorithms, operations research, heuristic optimization. Advanced optimization techniques: branch-and-cut, branch-and-price, metaheuristics.  Focus will be placed on classical transportation problems such as TSP, vehicle routing, shipment.

## Subject of course

Basics: modeling, algorithms, operations research, heuristic optimization. Advanced optimization techniques: branch-and-cut, branch-and-price, metaheuristics. Classical transportation problems: TSP, vehicle routing, shipment Didactics: - weekly lecture - exercise - oral exam

## Teaching methods

The VU is held in blocks on selected dates. In addition to the lecture units, students work through calculation examples at home on one date after preparation. A programming task serves to consolidate the course content. The VU concludes with an exam.

## Mode of examination

Written and oral

Estimated Effort:

15h  Lecture
10h  Homework
15h  Programming Exercise I
15h  Programming Exercise II
20h  Exam Preparation and Exam
----------------------------------------------
75h Total

## Course dates

DayTimeDateLocationDescription
Mon16:00 - 20:0004.03.2024 - 24.06.2024Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Optimization in Transport and Logistics - Single appointments
DayDateTimeLocationDescription
Mon04.03.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon11.03.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon18.03.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon08.04.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon15.04.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon22.04.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon29.04.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon06.05.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon13.05.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon27.05.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon03.06.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon10.06.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon17.06.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics
Mon24.06.202416:00 - 20:00Seminarraum FAV 01 C (Seminarraum 188/2) VU Optimization in Transport and Logistics

## Examination modalities

The exam will be based on the material covered during the course.

## Course registration

Begin End Deregistration end
23.02.2024 00:00 31.03.2024 00:00 31.03.2024 00:00

## Curricula

Study CodeObligationSemesterPrecon.Info
066 931 Logic and Computation Mandatory elective
066 937 Software Engineering & Internet Computing Mandatory elective

## Literature

• Toth, Paolo, and Daniele Vigo, eds. Vehicle routing: problems, methods, and applications. Vol. 18. Siam, 2014.
• Gendreau, M., & Potvin, J. Y. (2010). Handbook of metaheuristics (Vol. 2). New York: Springer.

## Previous knowledge

Requirements:

• knowledge of basic algorithms and data structures
• knowledge of linear algebra and analysis, especially set theory, metrics, sequences and series

English