Course content

Introduction to mathematical modelling for control and optimization of industrial systems. Problem formulation and choice of modelling. Linear, dynamic, non-linear and stochastic programming. Flow and network modelling. Queueing models and Markov chains. Some analytical results and use of discrete event simulation. Monte Carlo simulation. Stochastic inventory models. Reliability and maintenance of the production line. Synchronization of maintenance and production activities. Models and study of digital technologies for control and optimization of cyber physical systems. Digital twin and simulation of industrial systems. Basic control theory (cybernetics).

Learning outcome

Knowledge:

Basic knowledge about mathematical models of dynamic industrial systems. Know important terms like programming for optimization, queueing and Markov chain for control of industrial systems, simulation modelling, and basic control theory (cybernetics). Know what characterize dynamicity in industrial systems, and methods to analyze their behavior and controlling it with digital technologies. Be able to design/synthesize simple cyber physical systems thanks to digital twin and simulation of industrial systems. Be familiar with the most common digital technologies in industrial use.

Skills:

Be able to carry out small development projects independently and contribute actively in larger projects. Know how to control and optimize dynamic industrial systems. Design simple simulation models and digital twins. Have sufficient basic knowledge for more advanced control courses.

General competence:

Communicate about control and optimization-relevant issues both with specialists and the general public. Have conscious attitudes about how to control and optimize industrial systems, both technical and non-technical systems.

Learning methods and activities

Lectures, computer exercises and calculation exercises. There are eight calculation exercises, four of which have to be approved. Also three compulsory computer exercises in two-student groups, using MATLAB, Anylogic and Phyton. The course home page will be updated with summary of the lecture content. In addition, references to optional literature and some models and simple programs.

Compulsory assignments

• Exercises

Recommended previous knowledge

Basic knowledge in programming, statistics, probability theory and mathematical modelling.

Course materials

Course compendium to be downloaded from Blackboard. The course has a public home page.

Credit reductions