FACULTY OF ARTS AND SCIENCES
Department of Mathematics
SOCIAL MEDIA
IE 338 | Course Introduction and Application Information
| Course Name |
Stochastic Models in Manufacturing Systems
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 338
|
Fall/Spring
|
3
|
0
|
3
|
6
|
| Prerequisites |
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
First Cycle
|
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| Mode of Delivery | - | |||||||
| Teaching Methods and Techniques of the Course | Lecture / Presentation | |||||||
| Course Coordinator | ||||||||
| Course Lecturer(s) | ||||||||
| Assistant(s) | - | |||||||
| Course Objectives | The objective of this course is to purvey for the students of the following:Describe some important issues in the design and operation of manufacturing systems. Explain important measures of system performance. Show the importance of random, potentially disruptive events. Give some intuition about behavior of these systems. Explain the importance of capacity, and how it can vary randomly over time. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course deals with the following topics: Models of manufacturing systems, including transfer lines and flexible manufacturing systems; Calculation of performance measures, including throughput, inprocess inventory, and meeting production commitments; Realtime control of scheduling; Effects of machine failure, setups, and other disruptions on system performance. |
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|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Introduction: Basics of Probability | Lii J., and Meerkov, S. Production Systems Engineering, Ch 1, Springer, 2009. |
| 2 | Markov Chains and Processes | Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Ch 3, Prentice Hall, 1993 |
| 3 | The M/M/1 Queue | Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Ch 3, Prentice Hall, 1993 |
| 4 | Transfer Lines Models and Bounds | Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Ch 3, Prentice Hall, 1993 |
| 5 | Transfer Lines Models and Bounds (Continue) | Gershwin, Stanley B. Manufacturing Systems Engineering. Ch 2, Paramus NJ: Prentice Hall, 1993. |
| 6 | Deterministic Processing Time Transfer Line – 2 Machine | Gershwin, Stanley B. Manufacturing Systems Engineering. Ch 2, Paramus NJ: Prentice Hall, 1993. |
| 7 | Deterministic Processing Time Transfer Line – 2 Machine (Continue) | Gershwin, Stanley B. Manufacturing Systems Engineering. Ch 2, Paramus NJ: Prentice Hall, 1993. |
| 8 | Exponential Processing Time Transfer Line – 2 Machine | Gershwin, Stanley B. Manufacturing Systems Engineering, Ch 3, Paramus NJ: Prentice Hall, 1993. Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Ch 3. Prentice Hall, 1993. Lii J., and Meerkov, S. Production Systems Engineering, Springer, Ch 3, 2009. |
| 9 | Exponential Processing Time Transfer Line – 2 Machine (Continue) | Gershwin, Stanley B. Manufacturing Systems Engineering, Ch 3,. Paramus NJ: Prentice Hall, 1993. Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Ch 3, Prentice Hall, 1993 Lii J., and Meerkov, S. Production Systems Engineering, Springer, 2009. |
| 10 | Exponential Processing Time Transfer Line – 2 Machine (Continue) | Gershwin, Stanley B. Manufacturing Systems Engineering, Ch 3, Paramus NJ: Prentice Hall, 1993. Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Ch 3, Prentice Hall, 1993 Lii J., and Meerkov, S. Production Systems Engineering, Springer, C2009. |
| 11 | Deterministic Processing Time Transfer Line – Many Machines | Gershwin, Stanley B. Manufacturing Systems Engineering, Ch 3, Paramus NJ: Prentice Hall, 1993. Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Prentice Hall, Ch 3, 1993 |
| 12 | Deterministic Processing Time Transfer Line – Long Line Optimization | Gershwin, Stanley B. Manufacturing Systems Engineering, Ch 3,Paramus NJ: Prentice Hall, 1993. Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Prentice Hall, 1993 |
| 13 | Stochastic Long Lines | Gershwin, Stanley B. Manufacturing Systems Engineering, Ch 3, Paramus NJ: Prentice Hall, 1993. Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Prentice Hall, Ch 3, 1993 |
| 14 | Stochastic Long Lines | Gershwin, Stanley B. Manufacturing Systems Engineering, Ch 3, Paramus NJ: Prentice Hall, 1993. Buzacott, J.A and Shanthikumar, J. G. Stochastic Models of Manufacturing Systems, Prentice Hall, Ch 3, 1993 |
| 15 | Review of the semester | |
| 16 | Final Exam |
| Course Notes/Textbooks | The Course Material can be reached thru Course Web Pages. |
| Suggested Readings/Materials | Ana Ders Kitabı / Main Text Book : 1.Gershwin, Stanley B. Manufacturing Systems Engineering. Paramus NJ: Prentice Hall, 1993. ISBN: 9780135606087. or Manufacturing Systems Engineering, Stanley B. Gershwin, 2002. (gershwin@mit.edu, http://web.mit.edu/manufsys/www) Yardımcı Kitaplar / Supplementary References : 2. Stochastic Models of Manufacturing Systems, John A. Buzacott and J. George Shanthikumar, Prentice Hall, 1993. ISBN: 9780138475673 3. Production Systems Engineering, Jingshang Li and Semyon Meerkov, Springer, 2009. ISBN: 9780387755786 |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation |
1 – 15
|
5
|
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
5
|
10
|
| Presentation / Jury | ||
| Project |
1
|
20
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
25
|
| Final Exam |
1
|
40
|
| Total |
| Weighting of Semester Activities on the Final Grade |
60
|
|
| Weighting of End-of-Semester Activities on the Final Grade |
40
|
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
14
|
3
|
42
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
5
|
4
|
20
|
| Presentation / Jury |
0
|
||
| Project |
1
|
20
|
20
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
20
|
20
|
| Final Exam |
1
|
30
|
30
|
| Total |
180
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics. |
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| 2 | To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics. |
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| 3 | To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials. |
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| 4 | To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning. |
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| 5 | To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals. |
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| 6 | To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project. |
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| 7 | To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge. |
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| 8 | To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field. |
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| 9 | To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security. |
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| 10 | To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking. |
X | ||||
| 11 | To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language. |
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| 12 | To be able to speak a second foreign language at a medium level of fluency efficiently. |
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| 13 | To be able to relate the knowledge accumulated throughout the human history to their field of expertise. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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