IE306 Simulation
IE306 Simulation
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Industrial Engineering
TBA
Course Lecturer
Course Objectives
Introduce students to the concept of simulation and modeling. Present them the applications of stochastic simulation as a method for the design and analysis of systems. Enrich their knowledge about statistics and bring them closer to the knowledge of usage of the methods such as Monte Carlo simulation and Random Number Generation. Improve their computer skills and able them to run the simulation in Arena software.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Simulation,Sheldon M. Ross,Academic Press,2012; Simulation with Arena,W. David Kelton, Randall P. Sadowski, Nancy B. Swets, Mc.Graw Hill, 2001
Additional Literature
Teaching Methods
Lecture discussion and review questions; Short reports and homework assignments; Group discussions; Problem solving.
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction | ch1 |
| 2 | Element of Probability | ch2 (Ross) |
| 3 | Fundamental Simulation Concept | ch2 |
| 4 | Fundamental Simulation Concept | ch2 |
| 5 | Guided Thour through Arena | ch3 |
| 6 | Guided Thour through Arena | ch3 |
| 7 | Modeling Basic Operations and Inputs | ch4 |
| 8 | MIDTERM EXAM | |
| 9 | Random Numbers | ch3 (Ross) |
| 10 | Generating Discrete Random Variables | ch4 (Ross) |
| 11 | Generating Continuous Random Variables | ch5 (Ross) |
| 12 | The Discrete Event Simulation Approach | ch7 (Ross) |
| 13 | Statistical Analysis of Simulated Data | ch8 (Ross) |
| 14 | Markov Chain Monte Carlo Methods | ch12 (Ross) |
| 15 | Markov Chain Monte Carlo Methods | ch12 (Ross) |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 3 4
Homeworks
AI: Not AllowedAlignment with Learning Outcomes : 1 5
In-term Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 3
Assignment
AI: Not AllowedAlignment with Learning Outcomes : 2 5
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture Hours
45 hours ⏳ (15 week × 3 h)
Assignments
15 hours ⏳ (3 week × 5 h)
Home Study
30 hours ⏳ (15 week × 2 h)
Homework
25 hours ⏳ (5 week × 5 h)
In-term Exam Study
10 hours ⏳ (1 week × 10 h)
Final Exam Study
15 hours ⏳ (1 week × 15 h)
Assignment
10 hours ⏳ (1 week × 10 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [IE306] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| IE306 | Simulation | 3 | 2 | 6 | Tuesday:16.00-16.50 Thursday 14.00-15.50 | |||||
| Prerequisite | MATH203 | It is a prerequisite to | - | |||||||
| Lecturer | Office Hours / Room / Phone | Currently not available |
||||||||
| Assistant | MSc. Erna Omerasevic | Assistant E-mail | ekeskinovic@ius.edu.ba | |||||||
| Course Objectives | Introduce students to the concept of simulation and modeling. Present them the applications of stochastic simulation as a method for the design and analysis of systems. Enrich their knowledge about statistics and bring them closer to the knowledge of usage of the methods such as Monte Carlo simulation and Random Number Generation. Improve their computer skills and able them to run the simulation in Arena software. | |||||||||
| Textbook | Simulation,Sheldon M. Ross,Academic Press,2012; Simulation with Arena,W. David Kelton, Randall P. Sadowski, Nancy B. Swets, Mc.Graw Hill, 2001 | |||||||||
| Additional Literature | ||||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Lecture discussion and review questions; Short reports and homework assignments; Group discussions; Problem solving. | |||||||||
| Teaching Method Delivery | Teaching Method Delivery Notes | |||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction | ch1 | ||||||||
| Week 2 | Element of Probability | ch2 (Ross) | ||||||||
| Week 3 | Fundamental Simulation Concept | ch2 | ||||||||
| Week 4 | Fundamental Simulation Concept | ch2 | ||||||||
| Week 5 | Guided Thour through Arena | ch3 | ||||||||
| Week 6 | Guided Thour through Arena | ch3 | ||||||||
| Week 7 | Modeling Basic Operations and Inputs | ch4 | ||||||||
| Week 8 | MIDTERM EXAM | |||||||||
| Week 9 | Random Numbers | ch3 (Ross) | ||||||||
| Week 10 | Generating Discrete Random Variables | ch4 (Ross) | ||||||||
| Week 11 | Generating Continuous Random Variables | ch5 (Ross) | ||||||||
| Week 12 | The Discrete Event Simulation Approach | ch7 (Ross) | ||||||||
| Week 13 | Statistical Analysis of Simulated Data | ch8 (Ross) | ||||||||
| Week 14 | Markov Chain Monte Carlo Methods | ch12 (Ross) | ||||||||
| Week 15 | Markov Chain Monte Carlo Methods | ch12 (Ross) | ||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 3,4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Homeworks | 5 | 10 | 1,5 | Not Allowed | |
| In-term Exam | 1 | 30 | 1,3 | Not Allowed | |
| Assignment | 1 | 20 | 2,5 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Assignments | 5 | 3 | 15 | |||
| Home Study | 2 | 15 | 30 | Homework | 5 | 5 | 25 | |||
| In-term Exam Study | 10 | 1 | 10 | Final Exam Study | 15 | 1 | 15 | |||
| Assignment | 10 | 1 | 10 | |||||||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||