Systems Modeling

Faculty: Faculty of Engineering
Department(s): CE, IE, EE
Number of Students: 20
Course: Systems Modeling
Weekly hours: Theory: 0 Exercises: 2
ECTS Credits: 4
Semester: Fall

Lecture Schedule:

Monday: 08:15 - 10:00
Classroom: Online (via Zoom)

Lecturer: Dr. Hiqmet Kamberaj
Room Number:
Phone Number of the lecturer:
E-mail address of the lecturer: km.ude.ubi|jarebmakh#km.ude.ubi|jarebmakh

Course Objectives:

The aim of the course is to provide knowledge on the development of simulation system models and the ability to develop a simulation model for a given problem. This course provides the main concepts of modeling, which are state and state space, models of computation, concurrency, communication, modeling of data and time. The goal is to relate these concepts to applications and show the impact of the fundamental concepts on the potential and limitations of application techniques and tools, such as synthesis, performance analysis, formal verification, etc.

Learning Outcomes:

After completing this course, students will be able to:

  1. Model a process and design of computer simulation model.
  2. Use the random number generator in a simulation model.
  3. Use random variable generator in a simulation model.
  4. Learn how to analyze simulation output using statistics.
  5. Verify and validate a simulation model.
  6. Learn how to compare system configurations for statistical analysis purposes.

Skill outcomes Necessary ( + ) Not Necessary ( –)
Written communication skills +
Oral communication skills +
Computer skills +
Working in laboratory +
Working team +
Preparing projects +
Knowledge of foreign language +
Scientific and professional literature analysis +
Problem solving skills +
Management skills +
Presentation skills +

Course Textbooks:

  1. H. Kamberaj, Laboratory Lecture Notes in Systems Modeling Using Python, International Balkan University, 2020 (see also Laboratory Lecture Notes).
  2. F.L. Severance. System Modeling and Simulation. An Introduction.John Wiley & Sons, LTD, 2001.
  3. Law, A.M., and W.D. Kelton, “Simulation Modelling and Analysis”, McGraw Hill, Singapore (2000).
  4. Harrel, C.R., et. al., “System Improvement Using Simulation”, 3rd edition, JMI Consulting Group and ProModel Corporation (1995).

Teaching methods Ideal %
Teaching ex cathedra (teacher as the figure of authority, standing in front of the class and lecturing) 5
Interactive teaching (ask questions in class, assign and check homework, or hold class or group discussions) 10
Mentor teaching (consultant-teacher who has a supervisory responsibility and supervising the students) 5
Laboratory work 70
Seminar work -
Field Work (enables students to examine the theories and the practical experiences of a particular discipline interact) -
Semester project 5
Case Study (An in-depth exploration of a particular context) -
Students Team work 5


  • Students are obliged to attend at least 72 % out of 12 weeks of lectures, exercises, and other activities.
  • The attendance rule for failed overlapping courses is 36 % out of 12 weeks of lectures, exercises, and other activities.
  • The attendance rule for course from the upper semester is 57% out of 12 weeks of lectures, exercises, and other activities.
  • Students are not obliged to attend the course if the course is double repeated. However, they need to register the course.

Exams (Mid-Term Exam, Final Exam, Make-up Exam):

There are two exams, the Mid-Term and Final Exam, at the middle and at the end of the semester, respectively. The students, who do not earn minimum 50 credit points from the Mid-Term, Final Exam including Homework Assignments, have to take the Make-Up Exam, which counts only for Final Exam credit points. The terms of the exams are defined by the Academic Calendar announced on the University web site.

Passing Score:

The maximum number of credit points is collected during the semester, as follows: Mid-term Exam = 40 Credit Points (minimum requirement is 25 % (midterm exam + activity) to enter Final Exam), Final Exam (minimum requirement is 25 % to pass) = 40 Credit Points. Homeworks, quizzes, specific assignments and term papers = 20 Credit Points. Total=100.

Study Plan —- International Balkan University - Academic Calendar

~Week Lecture ~ Topics
1 1 Introduction to the philosophy of this course. Introduction to Python.
2 2 Data Types and Data Structures in Python.
3 3 Files and Coding in Python.
4 4 Modules, Functions, and Classes in Python.
5 5 Packages in Python. Describing a system in System modelling
6 6 System Integration.
7 - Mid Term Exam Week
8 7 Non-uniform Random number generators in Python
9 8 Random Processes.
10 9 Monte Carlo simulations
11 10 Random walks.
12 11 Modelling Covid-19 spread in closed area.
13 12 Simulation of Covid-19 spread in closed area
14 - Preparatory week.
15 - Make up exam week.

Student workload:

For calculating the Total Student Work Load we multiply the course ECTS credits with standard figure 30. (ECTS Credit: 4) x 30 = 120 hours.

Activities Hours
Lecture hours for 12 weeks: -
Laboratory and class exercises for 12 weeks: 24
Student Mentoring for 12 weeks: 14
Consultation for 12 weeks: 4
Exam preparations and exam hours (Midterm, final, Makeups) Exam preparations and exam hours: Each one should be at least minimum 10 hours. 30
Individual reading work for 12 weeks (Reading assignments/expectations for reading and comprehension is 5 pages per hour. Example: If a book 300 pages, total Individual reading work for 12 weeks 300:5 = 60 hours. 28
Homework and work practice for 12 weeks: 20
Preparation of diploma work, for 12 weeks: -
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