ECTS - Production Systems
Production Systems (IE509) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Production Systems | IE509 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Technical Elective Courses |
| 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 Lecturer(s) |
|
| 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;
|
| 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 |
Sources
| 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 | |
|---|---|
| Major Area Courses | |
| Supportive Courses | X |
| 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 | Ability to apply the acquired knowledge in mathematics, science and engineering | X | ||||
| 2 | Ability to identify, formulate and solve complex engineering problems | X | ||||
| 3 | Ability to accomplish the integration of systems | X | ||||
| 4 | Ability to design, develop, implement and improve complex systems, components, or processes | X | ||||
| 5 | Ability to select/develop and use suitable modern engineering techniques and tools | X | ||||
| 6 | Ability to design/conduct experiments and collect/analyze/interpret data | X | ||||
| 7 | Ability to function independently and in teams | X | ||||
| 8 | Ability to make use of oral and written communication skills effectively | X | ||||
| 9 | Ability to recognize the need for and engage in life-long learning | X | ||||
| 10 | Ability to understand and exercise professional and ethical responsibility | X | ||||
| 11 | Ability to understand the impact of engineering solutions | X | ||||
| 12 | Ability to have knowledge of contemporary issues | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 1 | 16 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 4 | 4 |
| Report | |||
| 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 | ||