Syllabus for Applied Systems Analysis
A revised version of the syllabus is available.
- 5 credits
- Course code: 1RT242
- Education cycle: First cycle
Main field(s) of study and in-depth level:
Sociotechnical Systems G2F,
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2007-03-15
- Established by: The Faculty Board of Science and Technology
- Applies from: Autumn 2007
Mathematics 20 points (30 ECTS credits). Probability theory MN1. Scientific computing NV1 and Computer Programming, first course.
- Responsible department: Department of Information Technology
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.
1. Discrete event simulation.
2. Modelling and simulation of continuous time models.
Lectures, problem solving sessions and home work assignments.
Written examination at the end of the course.
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Applies from: Autumn 2007
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