GRA 6227 Business Optimisation
GRA 6227 Business Optimisation
In this course, students will learn 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, using state-of-the-art software.
Students should develop skills in quantitative modelling of business problems and opportunities, and they should understand how such modeling techniques can be used to assist the decision-maker, when they are applicable, and what the main challenges in practical applications are.
Students should also get an understanding of why some problems are hard to solve while other problems can be easily solved using standard software.
Based on a given verbal description and numerical data for a decision problem, students should be able to define parameters and decision variables, identify the objective function and restrictions, formulate the corresponding mathematical model (LP, MIP or NLP), implement and solve the model using mathematical modelling software, and finally interpret and analyse the model results.
During this course, students will learn to appreciate 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 modeling and solving mathematical models (AMPL, R, Matlab, ...).
We will mainly use Python throughout the course as it has a rich ecosystem of packages for business analytics.
The students can choose other options but should expect less support if the lecturers are not familiar 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: Written submission 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: Submission Form of assessment: Written submission Invigilation 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.