Operations Research I (IE222) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Operations Research I IE222 3 2 0 4 7.5
Pre-requisite Course(s)
Math 275
Course Language English
Course Type N/A
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Drill and Practice, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Uğur BAÇ
  • Research Assistant İrem BULANIK ÖZDEMİR
  • Research Assistant Şevval KILIÇOĞLU
Course Assistants
Course Objectives Students should have the ability to model and solve real-life problems using linear programming techniques and analyze results obtained with such models. Students should be able to use software to solve a variety of models.
Course Learning Outcomes The students who succeeded in this course;
  • Students will acquire knowledge sufficient to use the deterministic O.R techniques, primarily the linear programming.
  • Students will be able to develop an appropriate model from a verbal description of a problem.
  • Students will be able to choose an approximate solution technique and solve engineering problems.
  • Students will be able to interpret relevant information from a model and/or a solution and interpret it.
  • Students will be able to develop and solve Linear Programming models using appropriate software packages.
Course Content Historical development of operations research, modeling, graphical solution, Simplex and dual Simplex methods, duality and sensitivity analysis, transportation, assignment, and transshipment problem.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to OR [1] pg. 1-9
2 A review of basic linear algebra [1] pg. 10-48
3 Introduction to Linear Programming [1] pg. 49-126
4 Introduction to Linear Programming The Graphical method [1] pg. 49-126 [1] pg. 50-99
5 The Graphical method [1] pg. 50-99
6 The Simplex algorithm [1] pg. 126-189
7 The Simplex algorithm [1] pg. 126-189
8 The Simplex algorithm [1] pg. 126-189
9 Sensitivity analysis [1] pg. 202-294
10 Sensitivity analysis [1] pg. 202-294
11 Midterm
12 Duality [1] pg. 295-334
13 Duality Transportation problems [1] pg. 295-334 [1] pg. 360-392
14 Transportation problems. Assignment and transshipment problems [1] pg. 360-392 [1] pg. 393-412
15 Assignment and transshipment problems [1] pg. 393-412
16 Assignment and transshipment problems [1] pg. 393-412


Course Book 1. Winston, W.L., Operations Research: Applications and Algorithms, 4th Edition, Brooks/Cole-Thomson Learning, 2004.
Other Sources 2. Frederick S. Hillier and Gerald J. Lieberman, Introduction to Operations Research and Revised CD-ROM 8, McGraw-Hill Science, 2005.
3. WU, N. and COPPINS, R., Linear Programming and Extensions, Cambridge University Press, 1981.
4. Anderson D. R., Sweeney D. J., and Williams T. A., An Introduction to Management Science, 11th Edition, West, 2004.
5. Taha, H. A., Operations Research: An Introduction, 8th Edition, Prentice Hall, 2006.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics 5 30
Homework Assignments - -
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 7 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply knowledge of mathematics, science and engineering to Industrial Engineering; an ability to apply theoretical and practical knowledge to model and solve engineering problems. X
2 An ability to identify, formulate and solve complex engineering problems; an ability to select and apply proper analysis and modeling methods. X
3 An ability to design a complex system, process, tool or component to meet desired needs within realistic constraints; an ability to apply modern design.
4 An ability to develop, select and put into practice techniques, skills and modern engineering tools necessary for engineering practice; an ability to use information technology effectively. X
5 An ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or disciplinary research topics. X
6 An ability to work individually, on teams, and/or on multidisciplinary teams.
7 Ability to communicate effectively in Turkish orally and in writing; knowledge of at least one foreign language; effective report writing and understand written reports, preparing design and production reports, making effective presentations, giving and receiving clear and understandable instruction.
8 A recognition of the need for, and an ability to engage in life-long learning; an ability to use information-seeking tools and to follow the improvements in science and technology.
9 An ability to behave according to the ethical principles, an understanding of professional and ethical responsibility. Information on standards used in industrial engineering applications.
10 Knowledge of business applications such as project management, risk management and change management. A recognition of entrepreneurship, innovativeness. Knowledge of sustainable improvement.
11 Information on the effects of industrial engineering practices on health, environment and security in universal and societal dimensions and the information on the problems of the in the field of engineering of the era. Awareness of the legal consequences of engineering solutions.
12 An ability to design, development, implementation and improvement of integrated systems that include human, materials, information, equipment and energy. X
13 Knowlede on appropriate analytical, computational and experimental methods to provide system integration.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory 16 2 32
Special Course Internship
Field Work
Study Hours Out of Class 16 4 64
Presentation/Seminar Prepration
Homework Assignments
Quizzes/Studio Critics 10 1 10
Prepration of Midterm Exams/Midterm Jury 1 12 12
Prepration of Final Exams/Final Jury 1 22 22
Total Workload 188