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
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.
Lectures, problem solving sessions and voluntary assignments.