BAN402 Decision Modelling in Business
This course is about formulating, analyzing and solving models for optimal decision making in business, using data and computer-based decision support. The formulation of the models is based on mathematical programming and optimization methods. To process data and solve the models, we use up-to-date computational tools specially designed to find the best decisions to a mathematical programming model.
The course focuses on problems that capture strategic, tactical, operational and economic aspects involved in the decision making of organizations. These include, for example, applications of decision modelling in business related to energy, natural resources and the environment, such as petroleum, electricity markets, forestry and standard logistic/distribution applications. Among these, we overview high-impact applications of decision models which have been recently developed for real-world problems, such as how to optimize the Norwegian natural gas production and transport, how forestry companies in Sweden can reduce costs and emissions by improving their transport operations, and how to find the equilibrium in the day-ahead market for trading power in the Nordic and Baltic regions.
The methods studied in the course come mainly from fields labeled as Operations Research, Management Science, and Prescriptive Analytics. Specific topics include linear programming, integer programming, nonlinear programming, economic interpretation, cooperative game theory, profit and cost sharing, equilibrium models, data uncertainty, computational optimization.
Four projects will be done individually or in groups of two students. Each project may consist of several parts: model formulation, implementation in software, interpretation and analysis of solution, article discussion and report writing.
By the end of this course the students
- are familiar with the application and impact of decision models in real-world problems
- are able to explain and discuss key concepts in decision making and optimization
- are able to understand decision modelling works published in major scientific journals and formulate relevant research questions
- are able to formulate decision-making problems into an optimization model
- have developed good analytical skills for decision making in business
- are able to identify, analyze and process the data needed as input for a decision model
- have developed good skills to write codes and to cope with errors in a computational software
- are able to use computational tools for implementing and solving a decision model
- are able to analyze performance of decision making and solution quality by support of computational tools
Lectures, projects, lab sessions, software coding.
Credit reduction due to overlap
Course identical to BUS461 (former ENE420)
The final grade will be based on the project reports. The project reports must be written in English. Four projects will be done individually or in groups of two students.
Project 1: Handed out in week 37, deadline in week 40
Project 2: Handed out in week 38, deadline in week 40
Project 3: Handed out in week 41, deadline in week 44
Project 4: Handed out in week 42, deadline in week 44
This course is a continuation of BUS461 and the total number of attempts applies to the course (not the course code).
Standard laptop, AMPL modelling language with solvers CPLEX, Gurobi and MINOS (licenses will be provided during the course), data processing in Microsoft Excel, VBA macros in Excel, Excel Optimization solver.
- R. Fourer, D. M. Gay and B. W. Kernighan, AMPL A Modeling Language for Mathematical Programming, 2nd Ed., Brooks/Cole-Thomson Learning, 2003. Available for free download on: http://ampl.com/resources/the-ampl-book/chapter-downloads/
- Articles/Reports/Chapters that describe specific applications (available through Its Learning/ handed out in Lectures)
- J. Lundgren, M. Rönnqvist and P. Värbrand, Optimization, Studentlitteratur, 2010. (Optional)
- ECTS Credits
- Teaching language
Autumn. Offered autumn 2019.
Associate Professor Mario Guajardo, Department of Business and Management Science