Entry requirements

120 credits including Linear algebra II, Probability and statistics, Scientific computing II and Automatic control I.

Learning outcomes

Students that pass the course should be able to

• understand and to give a survey of the parts of the systems analysis approach, from problem specification, through modelling, validation, problem solving techniques, to result evaluation, presentation of results and implementation
• formulate mathematical models of real-life problems in continuous and discrete time
• simulate continuous time and discrete time systems from their mathematical models using available software, and to analyse the outputs of simulations by relevant statistical methods
• use simulations to analyse system properties with respect to e.g. stability and the effect of feedback
• formulate and solve certain types of optimisation problems using linear programming and dynamic programming
• work with both the primal and dual forms of a linear programming problem, and to extract and use sensitivity information in the simplex tableau

Content

The systems analysis approach to model based problem solving, including problem specification, modelling, validation, problem solving techniques and result evaluation. Emphasis on finding suitable techniques for solving practical problems in working life. Different methods from systems analysis and operations research including optmisation, queuing analysis and simulation. The presentation of optimisation methods is based on practical problems, and mainly linear problems are treated. Introduction to the simplex method. Basic principles and applications of time-controlled, event-controlled and object oriented /pseudoparallel simulation. Statistical methods, e.g. pseudo-number generators, variance reduction techniques and sensitivity analysis. Analysis of equilibria and stability of nonlinear dynamic systems.

Instruction

Lectures, problem solving sessions and voluntary assignments.

Assessment

Written examination at the end of the course.