IE303 Operations Research I
IE303 Operations Research I
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Industrial Engineering
Özge Büyükdağlı
Course Lecturer
Course Objectives
Operations research (OR) has many applications in science, engineering, economics, and industry. The ability to solve OR problems is crucial for both researchers and practitioners. Being able to solve the real life problems and obtaining the right solution requires understanding and modeling the problem correctly and applying appropriate optimization tools and skills to solve the mathematical model. The goal of this course is to teach you to formulate, analyze, and solve mathematical models that represent real-world problems. We will also discuss how to use spreadsheets and other software packages for solving optimization problems.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
Operations Research: An Introduction, 11th edition, 2024, by Hamdy A. Taha, Pearson
Additional Literature
Introduction to Operations Research, 11th edition, by Hillier and LiebermanTeaching Methods
Interactive lectures with real-life engineering and data-driven examples
Problem solving sessions
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction to the course | Syllabus |
| 2 | Overview of the Operations Research Modeling Approach | Chapter 1-2, Hillier and Lieberman |
| 3 | Introduction to Linear Programming, solving using graphical method | Chapter 3, Hillier and Lieberman |
| 4 | Solving Linear Programming Problems | Chapter 3, Hillier and Lieberman |
| 5 | Solving Linear Programming Problems: using optimization software | Chapter 3, Hillier and Lieberman |
| 6 | Sensitivity analysis | |
| 7 | Introduction to Integer Programming | Chapter 12, Hillier and Lieberman |
| 8 | MIDTERM | |
| 9 | The Transportation and Assignment Problems | Chapter 9, Hillier and Lieberman |
| 10 | Network Optimization Models | Chapter 10, Hillier and Lieberman |
| 11 | Inventory Modeling (with Introduction to Supply Chains) | Chapter 13, Taha |
| 12 | Decision analysis | Chapter 15, Taha |
| 13 | Game Theory | Chapter 15, Hillier and Lieberman |
| 14 | Heuristic Programming | Chapter 10, Taha & Chapter 14, Hillier and Lieberman |
| 15 | Review, Preparation for the Final Exam |
Course Schedule (All Sections)
| Section | Type | Day 1 | Venue 1 | Day 2 | Venue 2 |
|---|---|---|---|---|---|
| IE303.1 | Course | Wednesday 12:00 - 14:50 | B F2.15 - Amphitheater II | - | - |
| IE303.1 | Tutorial | Thursday 12:00 - 13:50 | B F2.17 | - | - |
Office Hours & Room
| Day | Time | Office | Notes |
|---|---|---|---|
| Monday | 15:00 - 17:00 | A F1.8 | |
| Tuesday | 12:00 - 17:00 | A F1.8 | |
| Wednesday | 15:00 - 17:00 | A F1.8 |
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6
Project
AI: Consult InstructorAlignment with Learning Outcomes : 1 2 3 4 5 6
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6
Quiz
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4 5 6
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)
Project
30 hours ⏳ (6 week × 5 h)
Home Study
30 hours ⏳ (15 week × 2 h)
Active Tutorials
20 hours ⏳ (10 week × 2 h)
Midterm Exam Study
10 hours ⏳ (2 week × 5 h)
Final Exam Study
15 hours ⏳ (3 week × 5 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 [IE303] 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 | |||||||||
| IE303 | Operations Research I | 3 | 2 | 6 | ||||||
| Prerequisite | MATH201 | It is a prerequisite to | IE304 | |||||||
| Lecturer | Özge Büyükdağlı | Office Hours / Room / Phone | Monday: 15:00-17:00 Tuesday: 12:00-17:00 Wednesday: 15:00-17:00 |
|||||||
| obuyukdagli@ius.edu.ba | ||||||||||
| Assistant | Assistant E-mail | |||||||||
| Course Objectives | Operations research (OR) has many applications in science, engineering, economics, and industry. The ability to solve OR problems is crucial for both researchers and practitioners. Being able to solve the real life problems and obtaining the right solution requires understanding and modeling the problem correctly and applying appropriate optimization tools and skills to solve the mathematical model. The goal of this course is to teach you to formulate, analyze, and solve mathematical models that represent real-world problems. We will also discuss how to use spreadsheets and other software packages for solving optimization problems. | |||||||||
| Textbook | Operations Research: An Introduction, 11th edition, 2024, by Hamdy A. Taha, Pearson | |||||||||
| Additional Literature |
|
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| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Interactive lectures with real-life engineering and data-driven examples. Problem solving sessions. | |||||||||
| Teaching Method Delivery | Face-to-face | Teaching Method Delivery Notes | ||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction to the course | Syllabus | ||||||||
| Week 2 | Overview of the Operations Research Modeling Approach | Chapter 1-2, Hillier and Lieberman | ||||||||
| Week 3 | Introduction to Linear Programming, solving using graphical method | Chapter 3, Hillier and Lieberman | ||||||||
| Week 4 | Solving Linear Programming Problems | Chapter 3, Hillier and Lieberman | ||||||||
| Week 5 | Solving Linear Programming Problems: using optimization software | Chapter 3, Hillier and Lieberman | ||||||||
| Week 6 | Sensitivity analysis | |||||||||
| Week 7 | Introduction to Integer Programming | Chapter 12, Hillier and Lieberman | ||||||||
| Week 8 | MIDTERM | |||||||||
| Week 9 | The Transportation and Assignment Problems | Chapter 9, Hillier and Lieberman | ||||||||
| Week 10 | Network Optimization Models | Chapter 10, Hillier and Lieberman | ||||||||
| Week 11 | Inventory Modeling (with Introduction to Supply Chains) | Chapter 13, Taha | ||||||||
| Week 12 | Decision analysis | Chapter 15, Taha | ||||||||
| Week 13 | Game Theory | Chapter 15, Hillier and Lieberman | ||||||||
| Week 14 | Heuristic Programming | Chapter 10, Taha & Chapter 14, Hillier and Lieberman | ||||||||
| Week 15 | Review, Preparation for the Final Exam | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 30 | 1,2,3,4,5,6 | Not Allowed | |
| Semester Evaluation Components | |||||
| Project | 1 | 30 | 1,2,3,4,5,6 | Consult Instructor | |
| Midterm | 1 | 20 | 1,2,3,4,5,6 | Not Allowed | |
| Quiz | 2 | 20 | 1,2,3,4,5,6 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture Hours | 3 | 15 | 45 | Project | 5 | 6 | 30 | |||
| Home Study | 2 | 15 | 30 | Active Tutorials | 2 | 10 | 20 | |||
| Midterm Exam Study | 5 | 2 | 10 | Final Exam Study | 5 | 3 | 15 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 18/03/2026 | |||||||||