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
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.
Course Description 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 Content 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
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
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
Study Hours Out of Class
15
3
Field Work
Quizzes / Studio Critiques
Homework / Assignments
5
4
Presentation / Jury
Project
1
52
Seminar / Workshop
Oral Exam
Midterms
1
2
Final Exam
1
3
    Total
170

 

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, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

2

To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

3

To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively.

5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems.

6

To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

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 be able to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language.

12

To be able to speak a second foreign language 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 Software Engineering.

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