EASTERN MEDITERRANEAN UNIVERSITY
Department of Industrial Engineering
IENG314 Operations Research II
COURSE OUTLINE
COURSE CODE
COURSE TITLE
CREDIT VALUE
PREREQUISITES
DURATION OF COURSE
IENG314
Operations Research II
(4, 1, 0) 4
MATH322
One semester
COURSE LEVEL
COURSE TYPE
ECTS VALUE
COREQUISITES
Semester and year
Third year
Department Core
6
IENG313
Fall
WEB LINK
http://ie.emu.edu.tr/lec/ann.php?lec=Sahand+DANESHVAR
Instructor(s)
Assistant(s)
Name (group)
Assist. Prof. Dr. Sahand DANESHVAR,
Arman Nedjati
Mahmoud Golabi
e-mail
sahand.daneshvar@emu.edu.tr
Arman.nedjati@cc.emu.edu.tr
Mahmoud.golabi@cc.emu.edu.tr
2013 – 2014
Office
C109
C207
C209
Telephone
2773
1055
2820
CATALOGUE DESCRIPTION
This course introduces uncertainty, risk, and probabilistic approaches to Operations Research. Elementary mathematical models and topics
to be covered in this course are : review of probability theory with illustrations from inventory; decision analysis; decision trees and Bayes
rule; utility theory approach; Markov chain models, Chapman-Kolmogorov equations, steady-state probabilities and their computation, and
applications; M/M/c infinite and finite capacity queuing models and optimization, queuing networks; two-person, constant and non-constant
sum games , their analysis and applications.
AIMS & OBJECTIVES
1)
To help students to understand the importance of non-deterministic modelling of real life phenomena.
2)
To develop the skills for modelling and decision making under uncertainty.
3)
To appreciate the use of utility theory and game theory in decision making.
4)
To teach students the use of Markov chains and Poisson process in Probabilistic models.
5)
To understand and appreciate the use of queuing theory as powerful analytical tool.
6)
Use computer software for solving real-life problems, and interpret the results.
GENERAL LEARNING OUTCOMES (COMPETENCES)
On successful completion of the course, a student should have sufficient knowledge and understanding of:
o Probabilistic modelling of simple inventory models (Course objective (CO): 1)
o Decision making under uncertainty,(CO: 1,2,3)
o Utility theory approach to decision making, (CO: 3)
o Use of decision tree as an optimization tool, (CO: 2,3)
o Game theory models and their analysis, (CO: 3)
o Markov chain models as a powerful tool, (CO: 4)
o Poisson process models, (CO: 4)
o Queuing models and optimization. (CO: 5)
On successful completion of the course, students are expected to develop their skills in:
o Formulation of probabilistic and stochastic models arising in diverse real-life problems, (CO: 4,5)
o Solving queuing theory, game theory, and Markov chain models, (CO: 4,5)
o Optimal decision making under uncertainty,(CO: 1,2,3)
o Using related computer software effectively,(CO: 6)
o Using Poisson process as a powerful tool. (CO: 5)
On successful completion of the course, students are expected to develop their appreciation of and respect for values and attitudes regarding
the issue of:
o Role of stochastic modeling in real-life situations, (CO: 4,5)
o Impact of uncertainty in optimal decision making, (CO: 2,3)
o The role of Poisson process & Markov Chains, (CO: 4,5)
o Impact of computer software in obtaining solutions. (CO: 6)
LEARNING TEACHING METHODS
The function of teaching is to enable students to learn. Therefore students are required to read the chapters of the textbook before coming to
class and solve the related end of chapter questions after each lecture. The instructor will lecture in class by writing on the board and some
lectures will be given as a MS power point presentation.
ASSIGNMENTS
Students will be submited 2 homeworks before and after midterm exam recpectively.
METHOD OF ASSESSMENT
All Examinations will be based on lectures, discussions, textbook and assigned work. To enter a formal examination, a student has to present
her/his EMU student Identification card to the invigilator.
Attendance: Each student can take between 0-4% of total point by his/her continuous attendance.
Quizzes: There will be four quizzes designed to test familiarity and basic understanding of various topics. The best three between these four
will be considered in final point calculation. There will be no quiz make-ups.
Midterm Exam: The midterm exam will be held in the week designated by the university administration. It will cover all of the material up
to the date of examination.
Final Exam: The final exam will cover the whole course material. In form it will be a longer version of the midterm exam.
Make-up Exams: Make-up examinations will only be offered to students who provided adequate documentation for the reason of their
absence within four working days at the latest after the examination date. One final exam type make-up exam will be offered after the final
exams for the missed midterm and/or final exam. University regulations apply for graduation make-ups.
Any objection to the grade or mark should be made latest within a week following its announcement.
Grading Policy:
Attendance:
Homework:
Quizzes:
Midterm Exam:
Laboratory:
Final Exam:
4%
2%+2%
5 %+5% + 5%
26 %
15 %
36 %
Note that the instructor reserves the right to modify these percentages in case it is found necessary. You will be informed from the changes, if any.
ATTENDANCE
Attendance will be taken every lecture hour. Note that university regulations allow instructors to give a grade of NG (Nil Grade) to a student
whose absenteeism is more than 30% of the lecture/lab hours and/or who do not complete sufficient work that are included in the assessment
of the course.
TEXTBOOK/S
Wayne L. Winston, Operations Research – Applications and Algorithms, Duxbury Press, 4th edition, 2004.
J. K. Sharma, Operations Research – Theory and Applications, Macmillan India Ltd, 3th edition, 2007.
REFERENCES (available at EMU Library)

Frederick S. Hiller and G. L. Lieberman, Introduction to Operations Research, McGraw-Hill, 9th edition, 2009.

Hamdy A. Taha, Operations Research: An Introduction, Prentice Hall, 7th edition, 2003.
Note that EMU Library has quite a good collection of books on operations research area both at the intermediate and advanced levels.
COURSE CONTENT AND SCHEDULE
Monday
Thursday
Friday
Office Hour
Week
1
Lecture Hall
Time
IE-D201
14:30-16:20
IE-D201
12:30-14:20
Lab
10:30-11:20
Thursdays 14:30-16:30
Additional Lab Classes: 28 October
2013 14:30-16:20
16 December 2013 14:30-16:20
Quiz 1:
Quiz 2:
Quiz 3:
Quiz 4:
31 October
11 November
19 December
30 December
2013
2013
2013
2013
12:30-13:20
14:30-15:20
12:30-13:20
14:30-15:20
Topics
2
- Review of Probability Theory Using advanced feature of the Performance Improvement
Decision Making under Uncertainty
3
Utility Theory
4
Utility Theory
5
Decision Trees
6
Decision Trees
Management Software (PIM-DEA).
7-8
Analytic Hierarchy Process (AHP)
8-9
Midterm Exam
10
Markov Chains
11
Markov Chains
12
Poisson Process
13
Queuing Models
14
Queuing Models
15
Game Theory
16 - 18
Final Exam
Contribution of the Course to meeting the requirements of Criterion 5
Mathematics and Basic Sciences: 50%
Engineering Science
: 30 %
Engineering Design
: 20%
General Education
: -
Relationship of Course to Program Outcomes
Program
Outcomes
Level of
Satisfaction of
the Program
Outcomes
: Not
Applicable
: Low Level
of Satisfaction
: High Level
of Satisfaction
(a) an ability to apply knowledge of mathematics, science and engineering
 
(b) an ability to design and conduct experiments, as well as to analyze and interpret data
 
(c) an ability to design a system, component, or process to meet desired needs within realistic
constraints such as economic, environmental, social, political, ethical, health and safety,
manufacturability, and sustainability
 
(d) an ability to function on multi-disciplinary teams
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(e) an ability to identify, formulate, and solve engineering problems
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(f) an understanding of professional and ethical responsibility
 
(g) an ability to communicate effectively
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(h) the broad education necessary to understand the impact of engineering solutions in a global,
economic, environmental, and societal context
(i) a recognition of the need for, and an ability to engage in life-long learning
(j) a knowledge of contemporary issues
(k) an ability to use the techniques, skills, and modern engineering tools necessary for
engineering practice
 
 
 
 
ACADEMIC HONESTY - PLAGIARISM
Cheating is copying from others or providing information, written or oral, to others. Plagiarism is copying without acknowledgement
from other people’s work. According to university by laws cheating and plagiarism are serious offences punishable with disciplinary
action ranging from simple failure from the exam or project, to more serious action (letter of official warning suspension from the
university for up to one semester). Disciplinary action is written in student records and may appear in student transcripts.
PLEASE KEEP THIS COURSE OUTLINE FOR FUTURE REFERENCE AS IT CONTAINS IMPORTANT INFORMATION