Syllabus Application
IE 411
Modeling and Analysis of Large Systems
Faculty
Faculty of Engineering and Natural Sciences
Semester
Fall 2025-2026
Course
IE 411 -
Modeling and Analysis of Large Systems
Time/Place
Time
Week Day
Place
Date
11:40-12:30
Tue
FASS-G052
Sep 29, 2025-Jan 3, 2026
12:40-14:30
Thu
FENS-L027
Sep 29, 2025-Jan 3, 2026
Level of course
Undergraduate
Course Credits
SU Credit:3, ECTS:6, Engineering:6
Prerequisites
IE 312
Corequisites
Course Type
Lecture
Instructor(s) Information
Esra Koca
- Email: ekoca@sabanciuniv.edu
Course Information
Catalog Course Description
Modeling and analysis of large systems by bringing together a multitude of operations research techniques. Students will be exposed to unstructured problems particular in the areas of telecommunications and energy systems and will operate in teams to understand the problem, to transform it into a model, to bring a solution to the model using appropriate solution techniques, translate the model solution to problem solution.
Course Learning Outcomes:
| 1. | Develop strong mathematical models for different systems. |
|---|---|
| 2. | Develop efficient solution methods to solve the optimization problems that arise in large systems. |
| 3. | Implement the solution methods using optimization software and tools. |
Course Objective
This course aims to equip students with a deep understanding of advanced modeling and solution techniques in Operations Research. The objectives are to:
1. Provide students with a conceptual framework for formulating and analyzing complex decision problems.
2. Cultivate an appreciation of the role of optimization in designing and managing large-scale systems.
3. Bridge the gap between theory and practice by highlighting how mathematical models and algorithms are applied in real-world contexts.
4. Encourage critical thinking about the strengths, limitations, and applicability of different modeling and solution approaches.
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1. Provide students with a conceptual framework for formulating and analyzing complex decision problems.
2. Cultivate an appreciation of the role of optimization in designing and managing large-scale systems.
3. Bridge the gap between theory and practice by highlighting how mathematical models and algorithms are applied in real-world contexts.
4. Encourage critical thinking about the strengths, limitations, and applicability of different modeling and solution approaches.