GRADUATE SCHOOL

M.SC. In Industrial Engineering (With Thesis)

IE 501 | Course Introduction and Application Information

Course Name
Mathematics for Operations Research
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
IE 501
Fall/Spring
3
0
3
7.5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
Second Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives The objectives of this course are to enable the students to review basic theoretical concepts of linear algebra, optimization, real analysis and functional analysis, to be exposed of operational research applications of these mathematical concepts. This course introduces the key role of mathematics in optimization and linear systems. It explains effective procedures for performing mathematical tasks that arise in many fields, including operations research, engineering, systems sciences, statistics, and economics.\nWe emphasize the basic concepts and methodologies, and include dozens of examples and applications.
Learning Outcomes The students who succeeded in this course;
  • Be able to resolve linear independence and find null spaces and factors of matrices.
  • Be able to determine existence of restricted solutions to linear equations and inequalities.
  • Be able to resolve definiteness of Hermitian and real symmetric matrices by Gaussian pivoting.
Course Description The course basically covers sets, functions, countability, compact sets, lim sup and lim inf, convergence in R, convergence in metric spaces, normed spaces, inner product spaces, convexity, and functions of several variables

 



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 Proof, contrapositives, converses, sets Course notes
2 Sets, functions, countability Course notes
3 Properties of R Course notes
4 Sequences in R, subsequences, limits of sequences Course notes
5 Infimum and supremum of a set, lim inf and lim sup Course notes
6 lim inf and lim sup, series Course notes
7 Metric spaces, open and closed sets Course notes
8 Compact sets, connected sets Course notes
9 Convergence in metric spaces Course notes
10 Continuous functions Course notes
11 Properties of continuous functions Course notes
12 Uniform continuity Course notes
13 Vector spaces Course notes
14 Normed spaces, inner product spaces Course notes
15 Functions of several variables Course notes
16 Course review

 

Course Notes/Textbooks

Course notes

Suggested Readings/Materials

- Erhan Çınlar and Robert J. Vanderbei, Mathematical Methods of Engineering Analysis, free e-book, 2000.

- Walter Rudin, Principles of Mathematical Analysis, 3rd edition, McGraw-Hill, 1976, ISBN: 0-07-054234-X

- Kenneth A. Ross, Elementary Analysis:The Theory of Calculus, 2nd edition, Springer, 2013, ISBN: 978-1-4614-6270-5

- Rangarajan K. Sundaram, A First Course in Optimization Theory, Cambridge University Press, 1996, ISBN:978-0-521-49770-1

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
6
20
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
35
Final Exam
1
45
Total

Weighting of Semester Activities on the Final Grade
55
Weighting of End-of-Semester Activities on the Final Grade
45
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
15
6
90
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
6
10
60
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
10
10
Final Exam
1
17
17
    Total
225

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have an appropriate knowledge of methodological and practical elements of the basic sciences and to be able to apply this knowledge in order to describe engineering-related problems in the context of industrial systems.

X
2

To be able to identify, formulate and solve Industrial Engineering-related problems by using state-of-the-art methods, techniques and equipment.

X
3

To be able to use techniques and tools for analyzing and designing industrial systems with a commitment to quality.

X
4

To be able to conduct basic research and write and publish articles in related conferences and journals.

X
5

To be able to carry out tests to measure the performance of industrial systems, analyze and interpret the subsequent results.

X
6

To be able to manage decision-making processes in industrial systems.

X
7

To have an aptitude for life-long learning; to be aware of new and upcoming applications in the field and to be able to learn them whenever necessary.

X
8

To have the scientific and ethical values within the society in the collection, interpretation, dissemination, containment and use of the necessary technologies related to Industrial Engineering.

X
9

To be able to design and implement studies based on theory, experiments and modeling; to be able to analyze and resolve the complex problems that arise in this process; to be able to prepare an original thesis that comply with Industrial Engineering criteria.

X
10

To be able to follow information about Industrial Engineering in a foreign language; to be able to present the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.

X

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

 


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