Course Syllabus for


ETS061F, 7.5 credits

Valid from: Spring 2017
Decided by: Professor Thomas Johansson
Date of establishment: 2017-05-04

General Information

Division: Electrical and Information Technology
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: ETS061
Teaching language: English


The purpose of the course is to give an introduction to discrete event simulation, basic optimization approaches, and heuristic methods such as simulated annealing, tabu search, evolutionary algorithms and GRASP.


Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must

Course Contents

In the course we start by studying discrete event simulation. Students learn to write process-oriented and event-scheduling simulation programs in general programming languages. Estimation of accuracy, random number generation, methods for studying rare events, verification and validation are also covered. Then we proceed to optimization techniques. We study convex problems and their duals. Further, we go to linear programs (LP), the simplex algorithm, and the column generation technique. We show how to model non-linearity. After that we consider integer programming (IP), its relation to LP, and the branch-and-bound method for IP. We also mention the cutting plane method for IP and sketch the computational complexity theory, including the notions of polynomial problems and NP-hardness. Finally, we consider heuristic methods for combinatorial optimization problems viewed as optimization through simulation. We explain the local search and the role of randomness. We explain the basic meta-heuristics such as simulated annealing, evolutionary algorithms, and GRASP. We also illustrate the Monte Carlo techniques.

Course Literature

Nyberg, C.: Kompendium i simulering..

Instruction Details

Types of instruction: Lectures, exercises

Examination Details

Examination formats: Written exam, written assignments. A pass grade requires passed assignments and passed home exam
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: Programming, Basic probability, Statistical methods, Mathematical analysis.

Further Information

Course coordinator, professor Björn Landfeldt

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page:

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