Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
7EDS401Industrial Engineering Solutions and Software Applications2+2+03405.01.2026

 
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Industrial Engineering
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course TThe aim of this course is to develop students' ability to systematically analyze and define industrial engineering problems, to construct appropriate mathematical models and to analyze these models using various software tools. With this course, it is aimed that students contribute to decision-making processes by solving real-life production, planning, logistics and optimization problems with computer-aided methods.
Course Content This course includes fundamental applications of heuristic and metaheuristic algorithms using the Lingo optimization program,POM-QM and Python programming language. Students will also develop software-based solutions for problems related to production systems, scheduling, supply chain management, project management, and quality management. The course also covers classification and clustering algorithms.
Course Methods and Techniques The course will include theoretical knowledge transfer as well as hands-on projects, group work, lab applications, and software usage. Students will work on weekly sample problems and actively participate in class. Additionally, students will be assessed through a midterm exam and a final exam.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof. Nilüfer Çelikkol nilufer.celikkol@gedik.edu.tr
Assistants Research Assist. Serkan Mutlu serkan.mutlu@gedik.edu.tr
Work Placement(s) No

Recommended or Required Reading
Resources "Introduction to Operations Research" by Frederick S. Hillier, Gerald J. Lieberman https://www.mheducation.com/highered/product/introduction-operations-research-hillier-lieberman/M9781259162985.html
"Python for Data Analysis" by Wes McKinney https://www.oreilly.com/library/view/python-for-data/9781491957653/
"Metaheuristics: From Design to Implementation" by El-Ghazali Talbi https://www.wiley.com/en-us/Metaheuristics%3A+From+Design+to+Implementation-p-9780470278581
"Production Planning and Industrial Scheduling" by D.R. Sule https://www.crcpress.com/Production-Planning-and-Industrial-Scheduling-Examples-Case-Studies-and-Solutions/Sule/p/book/9781574446202
"Supply Chain Management: Strategy, Planning, and Operation" by Sunil Chopra, Peter Meindl https://www.pearson.com/store/p/supply-chain-management-strategy-planning-and-operation/P100000328804
Course Notes Course notes will include materials provided by the instructor, along with lecture explanations and sample applications related to Lingo,POM-QM and Python. Additionally, sample problems and solutions for the weekly topics will be shared.

Course Category
Mathematics and Basic Sciences %30
Field %70

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 2 % 50
Final examination 1 % 50
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 2 28
Hours for off-the-c.r.stud 14 2 28
Mid-terms 2 5 10
Practice 14 2 28
Project 1 10 10
Final examination 1 5 5
Total Work Load   Number of ECTS Credits 4 109

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 With SPSS, students can easily perform statistical formulas seen in normal statistics lessons thanks to applied statistics. They can see the types of graphs in practice and learn to interpret events and make decisions through various analysis methods. They can learn the solution of the hypotheses put forward on a scientific basis.
2 To be able to express Statistical Concepts and Theories using software programs and to be able to make scientific reasoning with the ideas developed in this direction.
3 Students can model optimization problems with numerical methods, obtain optimal solutions by using appropriate solution techniques, and analyze the results obtained and use them in decision-making processes
4 Ability to manage and direct team coordination in projects using software applications, to conduct constructive self-criticism and to exhibit a positive approach to problems
5 Ability to report the results obtained using software and to make written comments from the inferences

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction and Course Overview
2 1. SPSS APPLICATIONS 1.4 What is SPSS? Overview of the SPSS interface, how to enter data in SPSS?
3 1.2 Deciding on normal distribution in SPSS, graph types
4 1.3 Application, interpretation and decision making of some parametric and non-parametric tests in SPSS
5 2. BASIC R KNOWLEDGE, DATA PREPROCESSING AND INFERENTIAL STATISTICS 2.1 Basic Operations in R Software Language (Data Types, Variables, Basic Arithmetic Functions, Use of Libraries) 2.2 Data Visualization
6 2.3 Data Manipulation and Data Preprocessing
7 2.4 Introduction to Inferential Statistics and Forecasting (Weighted Averages Method, Moving Averages Method, Exponential Smoothing, Holt Method)
8 Midterm Exam
9 3. APPLICATIONS WITH POM-QM AND LINDO SOFTWARE 3.1 Linear programming models and solutions 3.2 Simplex method computer applications
10 3.3 Applications of transportation and assignment problems 3.4 Goal programming applications 3.5 Network models applications
11 3.6 Game problems applications 3.7 Applications of inventory control models 3.8 Project planning and CPM-PERT applications
12 4. SUPERVISED LEARNING METHODS
13 5. UNSUPERVISED LEARNING METHODS
14 6. DESCRIPTIVE STATISTICS

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
C1 5 5
C2 5 5 5 5
C3 5 5
C4 5 5 5 5
C5 5 5 5

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  https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=234094&curProgID=5582&lang=en