IE 338 | Course Introduction and Application Information - Department of Software Engineering

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

National Occupation Classification

-

Course Coordinator

Dr. Öğr. Üyesi Kamil Erkan Kabak

Course Lecturer(s)

Dr. Öğr. Üyesi Kamil Erkan Kabak

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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	to define the meaning and scope of Stochastic Models in Manufacturing in a historical context
2	to explain important metrics that specify a system’s performance
3	to give examples from Queueing Networks and their applications
4	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
5	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
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
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

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
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

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

#	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, computer computation, and topics specific to related engineering disciplines; the ability to use this knowledge in solving complex engineering problems		-	-	-	-	-
	1	Mathematics	-	-	-	-	-
	2	Science	-	-	-	-	-
	3	Basic engineering	-	-	-	-	-
	4	Computer computation	-	-	-	-	-
	5	Topics specific to related engineering disciplines	-	-	-	-	-
	6	The ability to use this knowledge in solving complex engineering problems	-	-	-	-	-
2	Problem Analysis: The ability to define, formulate, and analyze complex engineering problems by using fundamental science, mathematics, and engineering knowledge, while considering the relevant UN Sustainable Development Goals (SDGs) related to the problem.		-	-	-	-	-
3	Engineering Design: The ability to design creative solutions to complex engineering problems; the ability to design complex systems, processes, devices, or products that meet present and future requirements, considering realistic constraints and conditions.		-	-	-	-	-
	1	The ability to design creative solutions to complex engineering problems	-	-	-	-	-
	2	Considering realistic constraints and conditions in designing complex systems, processes, devices, or products	-	-	-	-	-
	3	The ability to design in a way that meets current and future requirements	-	-	-	-	-
4	Use of Techniques and Tools: The ability to select and use appropriate techniques, resources, and modern engineering and information technology tools, including prediction and modeling, for the analysis and solution of complex engineering problems, while being aware of their limitations		-	-	-	-	-
5	Research and Investigation: The ability to use research methods, including literature review, designing experiments, conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems.		-	-	-	-	-
	1	The ability to use research methods, including literature review	-	-	-	-	-
	2	Designing experiments	-	-	-	-	-
	3	Conducting experiments, collecting data, analyzing and interpreting results, for the investigation of complex engineering problems	-	-	-	-	-
6	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs); awareness of the legal consequences of engineering solutions		-	-	-	-	-
	1	Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals (SDGs)	-	-	-	-	-
	2	Awareness of the legal consequences of engineering solutions	-	-	-	-	-
7	Ethical Behavior: Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility; awareness of acting impartially and inclusively, without discrimination in any matter. (FENG101)		-	-	-	-	-
	1	Acting in accordance with the principles of the engineering profession; knowledge of ethical responsibility	-	-	-	-	-
	2	Awareness of acting impartially and inclusively, without discrimination in any matter.	-	-	-	-	-
8	Individual and Team Work: The ability to work effectively as an individual and as a member or leader of both intra-disciplinary and interdisciplinary teams (whether 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), particularly in technical matters.		-	-	-	-	-
	1	Verbal (ENGxxx)	-	-	-	-	-
	2	Written effective communication skills. (ENGxxx)	-	-	-	-	-
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; (FENG497-FENG498)	-	-	-	-	-
	2	Awareness of entrepreneurship and innovation. (FENG101)	-	-	-	-	-
11	Lifelong Learning: The ability to learn independently and continuously, adapt to new and emerging technologies, and think critically about technological changes.		-	-	-	-	-

IE 338 | Course Introduction and Application Information

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