Basic Information

Course Code:

IE 2001

Course Name:

Operations Research I

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

6.00

Instructor:

Dr. Öğr. Üyesi Kamer ÖZGÜN

Course Objectives

The aim of this course is to develop students’ ability to model, analyze, and optimize engineering decision problems. In this context, students learn fundamental operations research methods, particularly linear programming, and apply these methods to solve decision problems.

Course Content

This course covers an introduction to operations research, linear programming models, integer programming, transportation models, and network models. The formulation of these models, as well as their implementation using optimization software, are emphasized.

Prerequisites / Corequisites

MATH 1001

Course Books / Materials / Recommended Resources

Course notes, presentations, and application files are provided through the Learning Management System (LMS). Relevant chapters from standard textbooks (e.g., Winston, W. L. & Goldberg, J. B., Operations Research: Applications and Algorithms, and Taha, H. A., Operations Research: An Introduction) are used as reference materials in this course.

Learning Outcomes

Code	Description
LO1	Identify operations research problems and construct appropriate mathematical models.
LO2	Formulate and solve linear and integer programming problems.
LO3	Formulate, solve, and analyze transportation and network problems.
LO4	Compare different optimization models and select appropriate solution approaches.
LO5	Solve mathematical programming problems using optimization software and interpret the results.

Weekly Course Content

Week	Content
1	Introduction to Operations Research and Modeling Concepts (LO1)
2	Mathematical Modeling and Linear Programming Formulation (LO1, LO2)
3	Mathematical Modeling and Linear Programming Formulation (LO1, LO2)
4	Graphical Solution of Linear Programming Problems (LO2)
5	Special Cases in Linear Programming (LO2)
6	Introduction to Solver and Modeling Applications (LO5)
7	Sensitivity Analysis – Graphical Approach (LO3, LO4)
8	Midterm Week
9
10
11
12
13
14	Goal Programming (LO2, LO4)
15	General Review and Problem Solving (LO1–LO5)

Workload Calculation

Activity	Count	Duration (Hours)	Total
Attendance	14	2.00	28.00
Practice	14	2.00	28.00
Pre-Class Individual Study	14	2.00	28.00
Post-Prectice Individual Study	14	3.00	42.00
Midterm Exam/Preparation	1	15.00	15.00
Final Exam/Preparation	1	20.00	20.00
Homework	3	6.00	18.00
Total Workload (Hours)			179
ECTS Credit (Workload / 25)			6

Assessment

#	Assessment Type	Contribution (%)
1	Homework	%30
2	Midterm Exam	%30
3	Final Exam	%40
TOTAL		%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4	LO5
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11

1

Low Contribution

2

Medium Contribution

3

High Contribution

Teaching and Learning Methods

#	Method Name	Description	Tools
1	Lecture (expository teaching), interactive discussion	Listening and taking notes.	Standard classroom technologies, multimedia tools (projector, computer, digital presentations)
2	Homework	Solving and analyzing homework questions using a computer and Excel.	Computer

Academic Integrity and Artificial Intelligence

Students are expected to comply with academic integrity rules. AI tools may be used only for learning purposes; submitting AI-generated content as one’s own work is prohibited. Students must be able to understand and explain their solutions. The use of AI tools during exams is not allowed.