To convey skills and knowledge of production planning and integration through aggregate planning, inventory management and scheduling.
Prerequisite(s)
IE4201 Operations Research I
Corequisite(s)
-
Special Requisite(s)
-
Instructor(s)
Professor Tülin Aktin
Course Assistant(s)
Res. Assist. Oğuz Emir
Schedule
Theory (A,B): Monday 12:00-14:45, Z-D-1; Practice (A): Friday 13:00-14:45, Z-D-3; Practice (B): Friday 15:00-16:45, Z-A-1
Office Hour(s)
Prof. Tülin Aktin, Monday 15:00-16:00, 2-A-02; Res. Assist. Oğuz Emir, Friday 10:00-12:00, 2-A-13
Teaching Methods and Techniques
- Lectures
- Problem solving sessions
- Term project
- Seminar from experts in the area / Technical visit to a factory
Principle Sources
- Johnson, L.A. and Montgomery, D.C., Operations Research in Production Planning, Scheduling, and Inventory Control, John Wiley & Sons, Inc., 1974.
- Hax, A.C. and Candea, D., Production and Inventory Management, Prentice-Hall, Inc., 1984.
- Sipper, D. and Bulfin, Jr., R.L., Production: Planning, Control, and Integration, McGraw-Hill, Inc., 1998.
- Heizer, J., Render, B. and Munson, C., Operations Management: Sustainability and Supply Chain Management, Global Edition, 14th Edition, Pearson, 2023.
Other Sources
-
Course Schedules
Week
Contents
Learning Methods
1. Week
Aggregate production planning (Spreadsheet and transportation methods)
Lecture, Problem solving session
2. Week
Aggregate production planning (Static mathematical models)
Lecture, Problem solving session
3. Week
Aggregate production planning (Dynamic mathematical models)
Lecture, Problem solving session
4. Week
Aggregate production planning (Dynamic mathematical models)
Lecture, Problem solving session
5. Week
Deterministic inventory management
Lecture, Problem solving session
6. Week
Deterministic inventory management
Lecture, Problem solving session
7. Week
Stochastic inventory management
Lecture, Problem solving session
8. Week
No class
9. Week
MIDTERM EXAM
10. Week
Stochastic inventory management
Lecture, Problem solving session
11. Week
Material Requirements Planning (MRP)
Lecture, Problem solving session
12. Week
Material Requirements Planning (MRP)
Lecture, Problem solving session
13. Week
Sequencing and scheduling problems in a manufacturing environment
Lecture, Problem solving session
14. Week
Sequencing and scheduling problems in a manufacturing environment
Lecture, Problem solving session
15. Week
Final exam
16. Week
Final exam
17. Week
Final exam
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
35
Quizzes
3
10
Project(s)
1
15
Final Exam
1
40
Program Outcomes
PO-1
Ability to apply theoretical and practical knowledge gained by Mathematics, Science and their engineering fields and ability to use their knowledge in solving complex engineering problems.
PO-2
Ability of determining, defining, formulating and solving complex engineering problems; for that purpose develop the ability of selecting and implementing suitable models and methods of analysis.
PO-3
Ability of designing a complex system, process, device or product under real world constraints and conditions serving certain needs; for this purpose ability of applying modern design techniques
PO-4
Ability of selecting and using the modern techniques and devices which are necessary for analyzing and solving complex problems in engineering implementations; ability of efficient usage of information technologies.
PO-5
Ability of designing experiments, conducting tests, collecting data and analyzing and interpreting the solutions to investigate of complex engineering problems or discipline-specific research topics.
PO-6
Ability of working efficiently in intra-disciplinary and multi-disciplinary teams; individual working ability and habits.
PO-7
Ability of verbal and written communication skills; and at least one foreign language skills, ability to write effective reports and understand written reports, ability to prepare design and production reports, ability to make impressive presentation, ability to give and receive clear and understandable instructions
PO-8
Awareness of importance of lifelong learning; ability to access data, to follow up the recent innovation in science and technology for continuous self-improvement.
PO-9
Conformity to ethical principles; knowledge about occupational and ethical responsibility, and standards used in engineering applications.
PO-10
Knowledge about work life implementations such as project management, risk management and change management; awareness about entrepreneurship and innovativeness; knowledge about sustainable development.
PO-11
Knowledge about effects of engineering applications on health, environment and security in global and social dimensions, and on the problems of the modern age in engineering; awareness about legal outcomes of engineering solutions.
Learning Outcomes
LO-1
Solve aggregate production planning problems with respect to different strategies using spreadsheet and transportation models. Formulate linear mathematical models for solving static and dynamic aggregate production planning problems. Define the decision variables, determine the objective function and the constraints.
LO-2
Determine the lot size of a single item with deterministic and constant demand, and calculate the total cost of the inventory policy. Solve lot sizing problems under resource constraints with multiple items.
LO-3
Calculate the order quantities and their timing decisions under dynamic demand.
LO-4
Identify shortage and overage costs, and solve single period stochastic inventory problems under several demand probability density functions. Determine reorder point and safety stock levels in case of different demand and lead time structures.
LO-5
Understand the basics of dependent demand inventory models, and prepare a material requirements planning table. Make netting calculations, and compute planned order quantities by means of different lot sizing techniques.
LO-6
Determine the best sequence of jobs with respect to different performance measures. Based on priority dispatching rules, solve general job shop scheduling problems by drawing a Gantt chart. Apply various algorithms to manage single machine, parallel machine, flow shop, and job shop scheduling problems.
