GRA 6227 Business Optimisation
GRA 6227 Business Optimisation
This course teaches students how to use mathematical modelling to support practical business management decisions. The course will give an introduction to the use of the most common modelling techniques for deterministic optimisation, such as linear programming (LP), integer programming (IP), mixed-integer programming (MIP) and nonlinear programming (NLP). Applications of these methods in logistics/operations, strategy, marketing, and finance will be demonstrated through exercises and using state-of-the-art software.
- List the main components of an optimisation model.
- Classify and discuss the different approaches to solving optimization problems.
- Report and discuss the impacts and challenges of projects implementing decision-support systems.
- Develop mathematical models for practical optimisation problems.
- Implement mathematical models using some programming language.
- Solve and analyze mathematical models and their solutions.
- Frequent modelling activities should help the students develop analytical and logical thinking.
- After completing the course, the students can reflect on the value of analytical precision in business decision making.
- The concept of a mathematical programming model
- Linear programming models and the importance of linearity
- How to interpret model output
- Multi-period planning models
- Integer and mixed-integer models
- Good and bad formulations
- Non-linear models
- Multi-objective models
- Heuristics
- Practical aspects of optimization
Software: Python or any option allowing you to model and solve mathematical models (AMPL, R, Matlab, etc.).
We will primarily use Python (specifically the Pyomo package) throughout the course as it has a rich ecosystem of packages for business analysis.
Students may choose other options but should expect less support if lecturers are unfamiliar with the selected option.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
All parts of the assessment must be passed in order to get a grade in the course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
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Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 30 Grouping: Group/Individual (1 - 3) Duration: 3 Week(s) Comment: Group assignment Exam code: GRA 62273 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: School Exam Form of assessment: Written School Exam - pen and paper Exam/hand-in semester: First Semester Weight: 70 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: - Exam code: GRA 62274 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.