Syllabus Application
Operations Research I
IE 311
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
Operations Research I - IE 311
Classroom:
FENS-L045
Level of course:
Undergraduate
Course Credits:
SU Credit:3.000, ECTS:6, Basic:2, Engineering:4
Prerequisites:
( MATH 201) and ( ENS 208)
Corequisites:
IE 311R
Course Type:
Lecture
Instructor(s) Information
Erhun Kundakcıoğlu
Course Information
Catalog Course Description
Linear and integer programming formulations; convex analysis; algorithmic design and the simplex method; duality and sensitivity; computer implementations.
Course Learning Outcomes:
| 1. | Model linear decision problems into an integer or continuous linear programming model. |
|---|---|
| 2. | Solve linear programming problems by means of the primal and dual simplex methods and be able to decide under which conditions one should apply which method. |
| 3. | Have a basic knowledge of the concept of duality in linear programming and its consequences. |
| 4. | Implement linear and integer programming formulations in a programming language. |
Course Objective
The objective of this course is to study the modeling and solution of decision problems with deterministic parameters using operations research techniques with a particular emphasis on solution algorithms and implementation.
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Course Materials
Resources:
Operations Research: Applications and Algorithms, 4th edition. W.L. Winston.
Thomson/Brooks/Cole, 2004.
Introduction to Operations Research, 9th edition. F.S. Hillier, G.J. Lieberman.
McGraw-Hill, 2010.
Thomson/Brooks/Cole, 2004.
Introduction to Operations Research, 9th edition. F.S. Hillier, G.J. Lieberman.
McGraw-Hill, 2010.
Technology Requirements:
Students will need to model, implement and solve linear and integer programs in lectures and recitations. We will use Gurobi solver with Python interface.
AI/LLM Usage Policy
All coursework in this class consists of lab and recitation assignments that must be completed during class sessions.
Students are not permitted to use AI-based tools (such as ChatGPT, Claude, Bard, Grammarly, or similar platforms) for any part of these graded assignments. This prohibition includes -but is not limited to- brainstorming, outlining, drafting, editing, paraphrasing, or generating solutions. All submitted work must reflect each student’s own independent effort and original thought.
This restriction applies uniformly across all lab and recitation work. Use of AI tools during class for any graded task is strictly prohibited.
Outside of class, students are welcome to explore AI tools to aid their understanding of course concepts. However, please be aware that these tools may produce inaccurate or incomplete explanations, particularly for mathematical optimization problems. Students remain fully responsible for mastering the material and verifying their understanding.
AI/LLM Usage Policy
All coursework in this class consists of lab and recitation assignments that must be completed during class sessions.
Students are not permitted to use AI-based tools (such as ChatGPT, Claude, Bard, Grammarly, or similar platforms) for any part of these graded assignments. This prohibition includes -but is not limited to- brainstorming, outlining, drafting, editing, paraphrasing, or generating solutions. All submitted work must reflect each student’s own independent effort and original thought.
This restriction applies uniformly across all lab and recitation work. Use of AI tools during class for any graded task is strictly prohibited.
Outside of class, students are welcome to explore AI tools to aid their understanding of course concepts. However, please be aware that these tools may produce inaccurate or incomplete explanations, particularly for mathematical optimization problems. Students remain fully responsible for mastering the material and verifying their understanding.