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;
  • Will be able to define the meaning and scope of Stochastic Models in Manufacturing in a historical context
  • Will be able to understand important metrics that specify a system’s performance
  • Will be able to become familiar with Queueing Networks and their applications
  • Will be able to understand the scope of variety of queueing models such as M/M/1, M/G/1, GI/G/1 and Open and Closed Networks
  • Will be able 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 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 Assembly – Disassembly Systems 1,2
16 Review of the Semester  

 

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
Portfolios
Midterms / Oral Exams
1
25
Final / Oral 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
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
Portfolios
Midterms / Oral Exams
1
2
Final / Oral Exam
1
3
    Total
170

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

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