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IE 512 | Course Introduction and Application Information
| Course Name |
Manufacturing Systems Analysis
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 512
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Second Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| National Occupation Classification | - | |||||
| 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. NOTE: but the knowlwdge at the level of MATH 223 or MATH 218 is strongly recommended. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation | Learning Outcome |
| 1 | Introduction: Basics of Probability | 3 | |
| 2 | Markov Chains and Processes | 2 | |
| 3 | The M/M/1 Queue | 2 | |
| 4 | Transfer Lines Models and Bounds | 1 | |
| 5 | Transfer Lines Models and Bounds (Continue) | 1 | |
| 6 | Deterministic Processing Time Transfer Line 2 Machine | 1 | |
| 7 | Deterministic Processing Time Transfer Line 2 Machine (Continue) | 1 | |
| 8 | Exponential Processing Time Transfer Line 2 Machine | 1,2,3 | |
| 9 | Exponential Processing Time Transfer Line 2 Machine (Continue) | 1,2,3 | |
| 10 | Exponential Processing Time Transfer Line 2 Machine (Continue) | 1,2,3 | |
| 11 | Deterministic Processing Time Transfer Line Many Machines | 1,2 | |
| 12 | Deterministic Processing Time Transfer Line Long Line Optimization | 1,2 | |
| 13 | Stochastic Long Lines | 1,2 | |
| 14 | Stochastic Long Lines | 1,2 | |
| 15 | AssemblyDisassembly Systems | 1,2 | |
| 16 | Review of the Semester |
| Course Notes/Textbooks | The Course Material can be reached through Course Web Pages. |
| Suggested Readings/Materials | 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) 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 |
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 |
15
|
6
|
90
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
4
|
10
|
40
|
| Presentation / Jury |
0
|
||
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
20
|
20
|
| Final Exam |
1
|
27
|
27
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
PC Sub | Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 |
To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems. |
-
|
-
|
-
|
-
|
X
|
|
| 2 |
To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment. |
-
|
-
|
-
|
X
|
-
|
|
| 3 |
To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality. |
-
|
-
|
X
|
-
|
-
|
|
| 4 |
To be able to conduct basic research and write and publish articles in related conferences and journals. |
-
|
-
|
-
|
-
|
X
|
|
| 5 |
To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results. |
-
|
-
|
-
|
X
|
-
|
|
| 6 |
To be able to manage decision-making processes in industrial systems. |
-
|
X
|
-
|
-
|
-
|
|
| 7 |
To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary. |
-
|
X
|
-
|
-
|
-
|
|
| 8 |
To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering. |
-
|
X
|
-
|
-
|
-
|
|
| 9 |
To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria. |
-
|
X
|
-
|
-
|
-
|
|
| 10 |
To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
-
|
X
|
-
|
-
|
-
|
|
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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