Course code:
GRA 6227
Department:
Accounting and Operations Management
Credits:
6
Course coordinator:
Karim Tamssaouet
Course name in Norwegian:
Product category:
Master
Portfolio:
Semester:
2023 Autumn
Active status:
Active
Level of study:
Master
Teaching language:
English
Course type:
One semester
Introduction

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.

Learning outcomes - Knowledge

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.

Learning outcomes - Skills

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.

General Competence

During this course, students will learn to appreciate the value of analytical precision in business decision making.

Course content
• 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
• Non-linear models
• Multi-objective models
• Heuristics
• Practical aspects of optimization
Teaching and learning activities

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.

Software tools
Software defined under the section "Teaching and learning activities".

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.

Qualifications

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
Assessments
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
ECTS
Resit:
Examination when next scheduled course
Exam category:
Submission
Form of assessment:
Written submission
Invigilation
Weight:
70
Grouping:
Individual
Support materials:
• All printed and handwritten support materials
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Duration:
3 Hour(s)
Comment:
-
Exam code:
GRA 62274