EBA 3610 Decision Modelling Using Spreadsheet
EBA 3610 Decision Modelling Using Spreadsheet
The course combines consideration of realistic decision-making problems with theory and methodologies in building and solving spreadsheet models. Examples from finance, marketing, supply chain and other business areas illustrate management science applications and solutions methods using spreadsheet tools widely used in most of the organizations.
During the course, students should have acquired:
- Basic knowledge in Spreadsheet Modeling to be able to formulate a model based on a decision problem
- Identify and apply the right method
- Interpret and analyze the results
- Basic knowledge of Optimization with Linear, as well as Mixed-Integer Optimization Programming Models.
After completing the course, students shall be able to:
- Build models in spreadsheet and analyse the results
- Identify and apply modelling approach to solve decision problems
- Use spreadsheets for various business applications
- Model and solve linear and Mixed-Integer Optimization problems and perform sensitivity analysis
- Model decisions under uncertainty.
The students will learn how to build a model, analyze a decision problem, evaluate the data and how to select a modelling and solution approach.
- Introduction to Spreadsheet Modeling.
- Introduction to Optimization and Linear Programming Models.
- Modeling and solving LP problems and sensitivity analysis.
- Network Models.
- Optimization Models with Integer Variables.
- Mixed-Integer Optimization
- Solution methods: Simplex and Evolutionary Solver.
- Decision Making Under Uncertainty
- Introduction to Simulations
- Multiobjective Decision Making.
- Inventory and Supply Chain Models.
The course will include/entail a combination of Lectures and exercises using Excel.
Students are expected to solve a number of spreadsheet exercises, which will be given after each lecture.
The students are recommended to use a PC, not Mac during the course.
There will be a re-sit examination in EBA 36101 last time autumn 2022. Please note that re-sit examination autumn 2022 will be arranged as a home exam, 4 hours.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
EBA 1180 Mathematics for Data Science, EBA 2904 Statistics or equivalent courses, Excel course.
| Assessments |
|---|
Exam category: Submission Form of assessment: Structured test Weight: 30 Grouping: Individual Duration: 24 Hour(s) Comment: Midterm multiple choice test with questions that require building models and solving those to be able to answer on the questions. All exams must be passed to obtain a final grade in the course. Exam code: EBA 36102 Grading scale: ECTS Resit: Examination every semester |
Exam category: Submission Form of assessment: Written submission Weight: 70 Grouping: Individual Duration: 4 Hour(s) Comment: Exam includes several tasks to be solved based on a spreadsheet. All exams must be passed to obtain a final grade in the course. Exam code: EBA 36103 Grading scale: ECTS Resit: Examination every semester |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 45 Hour(s) | |
Group work / Assignments | 96 Hour(s) | |
Student's own work with learning resources | 55 Hour(s) | |
Examination | 4 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.