Course syllabus


EITN95, 7,5 credits, A (Second Cycle)

Valid for: 2020/21
Decided by: PLED C/D
Date of Decision: 2020-03-30

General Information

Elective for: C4-ks, D4-ns, E4-ks, I4, M4, Pi4
Language of instruction: The course will be given in English on demand


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 and evolutionary algorithms.

Learning outcomes

Knowledge and understanding
For a passing grade the student must


Competences and skills
For a passing grade the student must

Judgement and approach
For a passing grade the student must



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 linear programs (LP) and the simplex algorithm. After that we consider integer programming (IP) and Mixed Integer Programming (MIP), the relation between IP and LP, and the branch-and-bound method for IP.

Finally, we consider heuristic and meta-heuristic methods for combinatorial optimization problems viewed as optimization through simulation. We explain the local search and its most common variations. We explain the basic meta-heuristics such as simulated annealing, tabu search and evolutionary algorithms. We also illustrate the Monte Carlo techniques.

Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Approved home assignments and laboratory exercices gives grade 3. An approved take-home examination is required for grades 4 and 5.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Code: 0119. Name: Home Assignments .
Credits: 6,5. Grading scale: TH. Assessment: For grade 3, approved home assignments are required. Home Exam is required for grade 4 and 5. Contents: Home Assignments
Code: 0219. Name: Laboratory Exercices.
Credits: 1. Grading scale: UG. Assessment: For course completion, passed laboratory work is required. Contents: Laboratory work


Assumed prior knowledge: Programming, Basic probability, Statistical methods, Mathematical analysis.
The number of participants is limited to: No
The course overlaps following course/s: ETS060, ETS120, ETS061

Reading list

Contact and other information

Course coordinator: Björn Landfeldt,
Course homepage: