FACULTY OF ENGINEERING

Department of Industrial Engineering

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
  IE 353 To succeed (To get a grade of at least DD)
Course Language
English
Course Type
Elective
Course Level
First Cycle
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;
  • to define the meaning and scope of Stochastic Models in Manufacturing in a historical context
  • to explain important metrics that specify a system’s performance
  • to give examples from Queueing Networks and their applications
  • to explain the scope of variety of queueing models such as M/M/1, M/G/1, GI/G/1 and Open and Closed Networks
  • to analyze real life examples which aims to improve the manufacturer's productivity and efficiency through better design
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.

 



Course Category

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
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 have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems.

X
2

To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques.

X
4

To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently.

X
5

To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas.

X
6

To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually.

X
7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions

8

To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions.

9

To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice.

10

To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language.

12

To be able to speak a second foreign at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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