Module 31-MM34-WiMa Data Science in Operations Research

Attention: This page shows a discontinued module offer.

Faculty

Person responsible for module

Regular cycle (beginning)

To be discontinued

Credit points and duration

12 Credit points

For information on the duration of the modul, refer to the courses of study in which the module is used.

Competencies

Students learn to apply quantitative methods of decision making in the field of Operations Research and Data Science for solving real-world decision problems. Students will be able to identify optimization problems present in many industrial contexts and model them using linear mathematical models and (meta)heuristics. Students will be able to identify appropriate solution methods for problems and provide advice/results to decision makers. Students will utilize hybrid techniques utilizing Data Science and Operations Research.

Content of teaching

The module covers the mathematical modeling of optimization problems with a special focus on the domain of logistics. "Classic" Operations Research problems are presented and constraint types/modeling tricks are discussed, e.g., logical constraints and piecewise linearization. Furthermore, network models are covered along with special matrix structures. The branch-and-bound algorithm and its generalization, branch-and-cut, are presented as solution methods for the constructed models. Numerous heuristics and metaheuristics are presented and analyzed, such as Genetic Algorithms, Ant Colony Optimization, Simulated Annealing, Tabu Search, etc. Algorithm configuration and selection techniques are covered, as well as hybridized methods using these approaches within metaheuristics and mixed integer-linear solvers. When possible, guest lectures from industrial experts provide further insight into the application of course techniques in the real world.

Recommended previous knowledge

  • Students should have basic knowledge of programming.
  • Knowledge of linear algebra is helpful, but not necessary.

Necessary requirements

—

Explanation regarding the elements of the module

Module structure: 1 bPr 1

Courses

Combining OR and Data Science
Type lecture
Regular cycle SoSe
Workload5 120 h (30 + 90)
LP 4
Metaheuristics
Type lecture
Regular cycle SoSe
Workload5 120 h (30 + 90)
LP 4
Operations Research Models
Type lecture
Regular cycle WiSe
Workload5 120 h (30 + 90)
LP 4

Examinations

e-written examination o. e-portfolio with final written examination o. written examination o. portfolio with final written examination
Allocated examiner Person responsible for module examines or determines examiner
Weighting 1
Workload -
LP2 -

Portfolio of three or four exercises or programming tasks (workload 10-15 working hours each), which are provided during the course, a group project accompanying the course (workload 20-30 working hours) and a final written (e-)exam (usually 60 minutes). The exercises and programming tasks as well as the group project supplement and deepen the content of the lecture.
An overall evaluation with a weighting of 40 (exercises or programming tasks) : 25 (group project) : 35 (final exam) is carried out.
Or: 90 minute written exam in which all 3 courses (lectures) are examined. There will be a final overall evaluation.
The person responsible for the module designates one or more persons authorized to take the module part examination as examiners.

Further notices

Bei diesem Modul handelt es sich um ein auslaufendes Angebot. Dieses Modul richtet sich nur noch an Studierende, die nach einer der nachfolgend angegebenen FsB Versionen studieren. Ein entsprechendes Angebot, um dieses Modul abzuschließen, wird maximal bis Ende des Wintersemesters 2026/2027 vorgehalten. Genaue Regelungen zum Geltungsbereich s. jeweils aktuellste FsB Fassung.
Bisheriger Turnus des Moduls war jedes Semester.

Die Veranstaltungen des Moduls werden in englischer Sprache angeboten.

The module is used in these degree programmes:

Degree programme Profile Recom­mended start 3 Duration Manda­tory option 4
Mathematical Economics / Master of Science [FsB vom 28.02.2025 mit Änderung vom 14.08.2026] Mathematics 1. o. 2. 2 semes­ters Compul­sory optional subject
Mathematical Economics / Master of Science [FsB vom 28.02.2025 mit Änderung vom 14.08.2026] Finance 1. o. 2. 2 semes­ters Compul­sory optional subject
Mathematical Economics / Master of Science [FsB vom 16.09.2019 mit Änderungen vom 15.11.2022, 01.03.2024, 10.12.2024 und 14.08.2026] Mathematics 1. o. 2. 2 semes­ters Compul­sory optional subject
Mathematical Economics / Master of Science [FsB vom 16.09.2019 mit Änderungen vom 15.11.2022, 01.03.2024, 10.12.2024 und 14.08.2026] Finance 1. o. 2. 2 semes­ters Compul­sory optional subject

Automatic check for completeness

The system cannot perform an automatic check for completeness for this module.

Previus version of this module

Legend

1
The module structure displays the required number of study requirements and examinations.
2
LP is the short form for credit points.
3
The figures in this column are the specialist semesters in which it is recommended to start the module. Depending on the individual study schedule, entirely different courses of study are possible and advisable.
4
Explanations on mandatory option: "Obligation" means: This module is mandatory for the course of the studies; "Optional obligation" means: This module belongs to a number of modules available for selection under certain circumstances. This is more precisely regulated by the "Subject-specific regulations" (see navigation).
5
Workload (contact time + self-study)
SoSe
Summer semester
WiSe
Winter semester
SL
study requirement
Pr
Examination
bPr
Number of examinations with grades
uPr
Number of examinations without grades
This academic achievement can be reported and recognised.
Non-official translation of the module descriptions. Only the German version is legally binding.