Production Systems (IE509) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Production Systems IE509 3 0 0 3 5
Pre-requisite Course(s)
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Instructor Dr. Göknur Arzu Akyüz
Course Assistants
Course Objectives This course is designed to enable students to become aware of major production planning concerns and decision chains, fundamental problem areas in production planning and control, planning hierarchy and the relations with the management activities.
Course Learning Outcomes The students who succeeded in this course;
  • Students will have an understanding of mathematical models of inventory management and scheduling problems.
  • Students will be able to use analytical tools and algorithms for production planning problems.
  • Students will be familiarized with convergence of algorithms and complexity issues for combinatorial problems.
  • Students will acquire the ability to summarize a technical paper in front of an audience.
Course Content Management and control of production function in organizational systems, concepts of materials management, master production scheduling and production planning from different perspectives, aggregate planning, lot sizing, scheduling in manufacturing systems, scheduling in service systems, design and operation of scheduling systems, material requirem

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Typical features of production planning problems. Decision making in production planning. Short-term, medium-term, and long-term planning.
2 Overview of mathematical models and optimization tools
3 Deterministic continuous review models with uniform demand. Quantity discount models. Multiple-item models.
4 Stochastic reorder point models. Periodic review models.
5 Lot-sizing models with dynamic demand.
6 Dynamic Programming approach. Wagner-Whitin principle for lot-sizing decisions.
7 Zangwill’s extension to models which include backlogging.
8 Aggregate planning. LP models for aggregate planning. Transportation Model approach to production planning problems.
9 Minimum cost flow network models for production planning. Non-linear cost functions.
10 Midterm
11 Overview of deterministic vs. stochastic and static vs. dynamic models of scheduling. Integer programming models of single machine problems, algorithms and heuristics.
12 Parallel machine models. Deterministic flow-shop and job-shop models.
13 Assembly-line balancing: formulation and heuristics.
14 Issues of computational complexity
15 Final Examination Period
16 Final Examination Period


Course Book 1. L.A. Johnson and D.C. Montgomery, Operations Research in Production Planning, Scheduling, and Inventory Control, John Wiley & Sons 1974.
Other Sources 2. E.A. Silver, D.F. Pyke, R. Peterson, Inventory Management and Production Planning and Scheduling, 3rd edition, Wiley 1998.
3. D. Sipper and R.L. Bulfin Jr., Production: Planning, Control and Integration, McGraw Hill, 1997.
4. M. Pinedo, Scheduling: Theory, Algorithms and Systems, 2nd edition, Prentice-Hall, 2002.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 3 100
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.
2 An ability to design and conduct experiments, as well as to analyze and interpret data.
3 An ability to design a system, component, or process to meet desired needs.
4 An ability to function on multi-disciplinary teams.
5 An ability to identify, formulate and solve engineering problems.
6 An understanding of professional and ethical responsibility.
7 An ability to communicate effectively.
8 An understanding the impact of engineering solutions in a global and societal context and recognition of the responsibilities for social problems.
9 Recognition of the need for, and an ability to engage in life-long learning.
10 Knowledge of contemporary engineering issues.
11 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
12 Skills in project management and recognition of international standards and methodologies
13 An ability to make methodological scientific research.
14 An ability to produce, report and present an original or known scientific body of knowledge.
15 An ability to defend an originally produced idea.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Special Course Internship
Field Work
Study Hours Out of Class 16 1 16
Presentation/Seminar Prepration
Project 1 4 4
Homework Assignments 4 4 16
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 16 16
Prepration of Final Exams/Final Jury 1 25 25
Total Workload 125