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	3	0	3	6

Prerequisites	IE 353 To be successful (To have received at least DD grade)
Course Language	English
Course Type	ELECTIVE_COURSE
Course Level	First Cycle
Mode of Delivery	Face-To-Face
Teaching Methods and Techniques of the Course	Lecture/Presentation
National Occupational Classification Code	-
Course Coordinator	Dr. Öğr. Üyesi Kamil Erkan Kabak
Course Lecturer(s)	Dr. Öğr. Üyesi Kamil Erkan Kabak
Assistant(s)	-

Course Objectives

This course aims to equip students with the ability to model uncertainties and random events in manufacturing processes; to understand the random variables and processes that affect the performance of manufacturing systems; to use appropriate mathematical modeling techniques for analyzing and improving these systems; and to interpret the results obtained using various performance metrics in order to make decisions related to system design and operations.

Learning Outcomes

The students who succeeded in this course;

Name	Description	PC Sub	* Contribution Level
Name	Description	PC Sub	1	2	3	4	5
LO1	Students will be able to explain the role of probability theory and Markov processes in manufacturing systems.	1.1			X
LO2	Students will be able to use the M/M/1 queue model to model waiting times and process flows in manufacturing systems.	4			X
LO3	Students will be able to model transfer lines with fixed processing times.	4			X
LO4	Students will be able to model transfer lines with random processing times.	4			X
LO5	Students will be able to compare the performance of production lines using long line optimization and random long line models.	4			X

Course Description

Markov processes, M/M/1 queue models, transfer lines with two or more machines having different processing times, long lines, and random long lines.

Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Managment Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Required Materials	Learning Outcome
1	Probability Theory Review	Li, J., Meerkov, S. M., and Zhang, L. (2010). Chp 1	LO1
2	Markov Chains and Processes	Buzacott, J.A and Shanthikumar, J. G. (1993) Chp 3	LO1
3	M/M/1 Queueing Models	Buzacott, J.A and Shanthikumar, J. G. (1993) Chp 3	LO2
4	Transfer Lines Models and Bounds	Gershwin, Stanley B. (1993) Chp 2	LO3
5	Transfer Lines Models and Bounds	Gershwin, Stanley B. (1993) Chp 2	LO3
6	Deterministic Processing Time Transfer Line – 2 Machine	Gershwin, Stanley B. (1993) Chp 2	LO3
7	Deterministic Processing Time Transfer Line – 2 Machine	Gershwin, Stanley B. (1993) Chp 2	LO3
8	Midterm Exam		-
9	Exponential Processing Time Transfer Line – 2 Machine	Gershwin, Stanley B. (1993) Başlık 3, Buzacott, J.A and Shanthikumar, J. G. (1993) Başlık 3, Li, J., Meerkov, S. M., and Zhang, L. (2010). Başlık 3	LO4
10	Exponential Processing Time Transfer Line – 2 Machine	Gershwin, Stanley B. (1993) Başlık 3, Buzacott, J.A and Shanthikumar, J. G. (1993) Başlık 3, Li, J., Meerkov, S. M., and Zhang, L. (2010). Başlık 3	LO4
11	Deterministic Processing Time Transfer Line – Many Machines	Gershwin, Stanley B. (1993) Başlık 3, Buzacott, J.A and Shanthikumar, J. G. (1993) Başlık 3,	LO4
12	Sabit İşlem Zamanlı Transfer Hatları – Uzun Hat Optimizasyonu	Gershwin, Stanley B. (1993) Başlık 3, Buzacott, J.A and Shanthikumar, J. G. (1993) Başlık 3,	LO5
13	Stochastic Long Lines	Gershwin, Stanley B. (1993) Başlık 3, Buzacott, J.A and Shanthikumar, J. G. (1993) Başlık 3, Li, J., Meerkov, S. M., and Zhang, L. (2010). Başlık 3	LO5
14	Stochastic Long Lines	Gershwin, Stanley B. (1993) Başlık 3, Buzacott, J.A and Shanthikumar, J. G. (1993) Başlık 3, Li, J., Meerkov, S. M., and Zhang, L. (2010). Başlık 3	LO5
15	Review		-
16	Final Exam		-

Course Notes/Textbooks	Gershwin S. B. (1993). Manufacturing Systems Engineering. PTR Prentice Hall Buzacott J. A and Shanthikumar J. G. (1993). Stochastic models of manufacturing systems Li J. & Meerkov S. (2010). Production Systems Engineering. Springer.
Suggested Readings/Materials	-

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO1	LO2	LO3	LO4	LO5
Homework / Assignments	2	20	X	X	X	X	X
Project	1	20	X	X	X	X	X
Midterm	1	25	X	X	X	X	X
Final Exam	1	35	X	X	X	X	X
Total	5	100

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Participation	-	-	-
Theoretical Course Hours	16	3	48
Laboratory / Application Hours	-	-	-
Study Hours Out of Class	14	3	42
Field Work	-	-	-
Quizzes / Studio Critiques	-	-	-
Portfolio	-	-	-
Homework / Assignments	2	10	20
Presentation / Jury	-	-	-
Project	1	20	20
Seminar / Workshop	-	-	-
Oral Exams	-	-	-
Midterms	1	20	20
Final Exam	1	30	30
		Total	180

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.
	1	Mathematics			LO1
	2	Science
	3	Basic Engineering
	4	Computation
	5	Related engineering discipline-specific topics
	6	The ability to apply this knowledge to solve complex engineering problems
2	Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.
3	Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.
	1	Ability to design creative solutions to complex engineering problems
	2	Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions
4	Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.				LO2 LO3 LO4 LO5
5	Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.
	1	Literature research for the study of complex engineering problems
	2	Designing experiments
	3	Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.
	1	Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals
	2	Awareness of the legal implications of engineering solutions
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.
	1	Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility
	2	Awareness of being impartial and inclusive of diversity, without discriminating on any subject
8	Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).
	1	Ability to work individually and within the discipline
	2	Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote or hybrid)
9	Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession) on technical issues.
	1	Ability to communicate verbally
	2	Ability to communicate effectively in writing
10	Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
	1	Knowledge of business practices such as project management and economic feasibility analysis
	2	Awareness of entrepreneurship and innovation
11	Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.