| Detailed information |
| Original study plan |
Master's programme Economic and Business Analytics 2025W |
| Learning Outcomes |
Competences |
| Students are able to identify an optimization problem setting in a business context, develop a mathematical model defining the problem setting by themselves or in collaboration with domain and modeling experts, solve a mathematical model with appropriate tools, and analyze the solution of a model in terms of decisions for the business context.
Course Goals
The course aims to provide a practical experience in operations research (OR), complementary to the theory studied in the KS Operations Research. Students shall gain experience in modelling optimization problems arising from real-world situations. Programming tools to solve optimization problems are presented. The course provides practical experience in using these tools to solve their model, and translate back the output of these algorithms in terms of actual decision for the problem at hand.
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| Skills |
Knowledge |
- Learning Outcome 1 (LO1): Apply the key concepts of mathematical modeling to formulate an optimization problem from a verbally described problem setting. (k3)
- Learning Outcome 2 (LO2): Understand advanced modelling tricks for more complex business problems and use them to formulate optimization problems in similar contexts. (k3)
- Learning Outcome 3 (LO3): Solve optimization problems of small and medium sizes with a solver and interpret the results obtained. (k3)
- Learning Outcome 4 (LO4): Develop a simple algorithmic approach to generate a good solution for an optimization problem. (k6)
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- Linear and (mixed) integer programming (modelling linear optimization problems from business problems)
- Using solvers (using a programming language and a solver to solve linear optimization problems)
- Sensitivity analysis
- Heuristics
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| Criteria for evaluation |
In total, students have the possibility to reach 100 points, that can be obtained through two to three exams during the semester. A minimum of 50 points in total is necessary in order to obtain a positive grade. Bonus points may be obtained by voluntarily presenting homework solution in class when offered.
Final grades will be given as follows:
| Percent | Grade |
|---|
| 87,5 - 100 | 1 |
| 75 - 87 | 2 |
| 62,5 - 74,5 | 3 |
| 50 - 62,0 | 4 |
| 0 - 49,5 | 5 |
Synchronization of learning outcomes and assessments:
- LO1: Exam
- LO2: Exam
- LO3: Exam
- LO4: Exam
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| Methods |
The course uses a combination of different teaching methods in order to
- maximize the motivation and attention of the students.
- address the learning objectives in the didactically best way.
This includes the following
- Development of content in collaboration with the students on the black board based on exercise sheets.
- Development of step-by-step programming solutions with the students based on the same exercise sheets.
- Group homework exercises with personal and global feedback
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| Language |
English |
| Study material |
- Slides
- In-class modeling exercise sheets with detailed solutions
- Reading material: Hillier, Lieberman. Introduction to Operations Research. In the current edition.
- Pointers to additional literature
- Homework exercises
(All content is provided via moodle)
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| Changing subject? |
No |
| Corresponding lecture |
977PAMEOPRU19: IK Operations Research (2 ECTS)
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