Applied Systems Analysis
Syllabus, Bachelor's level, 1RT242
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Sociotechnical Systems G2F, Technology G2F
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 24 April 2008
- Responsible department
- Department of Information Technology
Mathematics 20 points (30 ECTS credits). Probability theory MN1. Scientific computing NV1 and Computer Programming, first course.
Students that pass the course should be able to
- understand and to give a survey of the basic 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
- formulate optimisation problems and solve linear programming problems using the Simplex method and appropriate optmisation software, and to extract and use sensitivity information in the simplex tableau, as well as to work with both the primal and dual forms of a linear programming problem
- formulate and solve certain types of optimisation problems using a dynamic programming approach
- generate a decision tree for the solution of certain types of decision-making problems
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 queuing analysis. Simulation as a method for problem solving. Basic principles and applications. Time-sequencing, time-controlled, event-controlled and object oriented /pseudoparallel simulation. Statistical methods, e.g. pseudo-number generators, variance reduction techniques and sensitivity analysis.
Lectures, problem solving sessions and voluntary assignments.
Written examination at the end of the course.