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 | 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 | 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 advanced knowledge in computational and/or manufacturing technologies to solve manufacturing engineering problems | |||||
| 2 | An ability to define and analyze issues related with manufacturing technologies | |||||
| 3 | An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment | |||||
| 4 | An ability to design a comprehensive manufacturing system based on creative utilization of fundamental engineering principles while fulfilling sustainability in environment and manufacturability and economic constraints | |||||
| 5 | An ability to chose and use modern technologies and engineering tools for manufacturing engineering applications | |||||
| 6 | Ability to perform scientific research and/or carry out innovative projects that are within the scope of manufacturing engineering | |||||
| 7 | An ability to utilize information technologies efficiently to acquire datum and analyze critically, articulate the outcome and make decision accordingly | |||||
| 8 | An ability to attain self-confidence and necessary organizational work skills to participate in multi-diciplinary and interdiciplinary teams as well as act individually | X | ||||
| 9 | An ability to attain efficient communication skills in Turkish and English both verbally and orally | |||||
| 10 | An ability to reach knowledge and to attain life-long learning and self-improvement skills, to follow recent advances in science and technology | |||||
| 11 | An awareness and responsibility about professional, legal, ethical and social issues in manufacturing engineering | |||||
| 12 | An awareness about solution focused project and risk management, enterpreneurship, innovative and sustainable development | |||||
| 13 | An understanding on the effects of engineering applications on health, social and legal aspects at universal and local level during decision making process | |||||
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 | ||