Examination arrangement

Course content

The course provides knowledge of methods and models for optimization based decision support under uncertainty and risk. The course consists of three related parts:

• Theory
• Algorithms and software
• Applications in finance, production planning, energy, telecommunications, among others

Learning outcome

Position and function within the study programme:

The course is designed for PhD students of the Department of Industrial Economics and Technology Management (IØT) and other departments who work with theoretical and practical optimization problems in different branches of industry and services with substantial uncertainty about problem data and other elements of problem formulation. The course is built upon optimization courses in IØT's master programme, knowledge of probability theory or similar knowledge.

The course will covey the following knowledge: The theoretical foundation necessary for formulation, analysis and solution of stochastic programming problems and relevant applications. The knowledge necessary to conduct research in the field of optimization under uncertainty.

The course will develop the following skills: Training to utilize optimization models for solution of planning and economic problems under uncertainty in energy, production, logistics, transportation, finance, telecom.

Other important learning objectives: Give training in implementation and use of relevant software for solution of optimization models under uncertainty.

Learning methods and activities

Lectures and exercises. Non-obligatory exercises. The course can be given in form of intensive lectures with several hours per day, several days per week, during a limited number of weeks in the semester.

Recommended previous knowledge

Knowledge of linear and nonlinear optimization is essential. Such knowledge can be obtained through courses TIØ4120 Operations Research, Introduction, TIØ4126 Optimization and Decision Support for Industrial Business Planning, or TIØ4130 Optimization Methods with Applications, or similar.

Required previous knowledge

Master of Science in Industrial Economics and Technology Management, or similar.

Course materials

Given at the beginning of the semester.

Credit reductions