IE 232 OPERATIONS RESEARCH I – MODELING (4 2 5) (ECTS: 7)
Spring 2016 - Tentative Syllabus
This is an introductory course and forms the modeling basis for several other industrial engineering courses
in both the undergraduate and the graduate curriculums of the department. This is the first of a series of
three Operations Research courses in our curriculum.
Catalogue Description. Principles of model building, basic optimization concepts and approaches for
problem solving are discussed in detail. Application of these principles and concepts are illustrated using
simplified but practical problems from diverse fields of applications in manufacturing/service/distribution
systems. Scope and limitations of suggested formulations are presented and their applications in reallife situations are focused on with the help of mathematical programming software. Emphasis is on the
formulation and interpretation of models rather than solution techniques.
Prerequisite. IE 101 Industrial Engineering Orientation.
Course Objectives. At the end of the course, students will develop:
• ability to formulate linear programming models including static and dynamic settings,
• ability to formulate integer programming models and skills in representing conditions using integer
variables,
• ability to formulate network flow programming models,
• ability to formulate goal programming models,
• ability to formulate some non-linear programming models,
• skills in using basic mathematical programming and optimization software and interpreting the solutions obtained.
Learning Outcomes. On successful completion of the course, all students will develop:
1. an understanding of the Operations Research approach to problem solving,
2. skills in model construction and analysis of various problems,
3. skills in using mathematical programming and optimization software packages,
4. skills in report writing.
On successful completion of the course, all students will be:
5. involved in team work,
6. aware of ethical issues.
Instructor.
Gonca Yıldırım, Office: L-320, Email: goncayildirim@cankaya.edu.tr,
Office Hour: Friday (15:20-16:10).
Teaching Assistant. Hale Akkocaoğlu, Office: L-327, Email: hale@cankaya.edu.tr,
Office Hour: Monday (16:20-17:10).
Class Meeting Times and Locations.
Section 1: Mon (09:20-11:10) in L-111, Wed (12:20-14:10) in M-102.
Section 2: Mon (11:20-13:10) in L-111, Wed (10:20-12:10) in K-A06.
Recitation & Lab Sessions and Locations.
Section 1: Tue (09:20 - 11:10) in H-302.
Section 2: Tue (11:20 - 13:10) in H-302.
Text. Winston, W. L. (2003). Operations Research: Applications and Algorithms. 4th edition. Cengage
Learning.
Supplementary Texts.
1. Hillier, F. S., and Lieberman, G. J. (2015). Introduction to Operations Research. 10th ed. McGraw
Hill Higher Education.
2. Taha, H. A. (2010). Operations Research: An Introduction. 9th ed. Prentice Hall.
3. Williams, H. P. (2013). Model Building in Mathematical Programming. 5th ed. John Wiley & Sons.
Class Website. Moodle on http://webonline.cankaya.edu.tr/
1
Honesty Policy. All students admitted to Çankaya University should declare his/her understanding and
belief in the Honor Code stated by the department for the examinations and assignments. This statement
is a reminder to uphold your obligation as a Çankaya University student and to be honest in all work
submitted and exams taken in this course and all others.
If you conduct any dishonest act during an examination or for the completion of an assignment
(i.e., cheating on an exam, using any extra material that you are not allowed to use during
an exam, copying material off of someone else’s homework or assignment, using solution keys
from previous years, copying material from published and electronic sources without paraphrasing
and/or citing appropriately), you will get a credit of 0 on that particular exam or assignment.
Necessary disciplinary action, as dictated by the rules of the University, will also be taken.
Class Policies.
• Please come to class on time. Do NOT enter the classroom if you are late and if I started
lecturing. Late arrivals as well as walking out and in during the lectures disrupt the flow of the
lecture. Please remember to turn off cellular telephones, tablets or any device that beeps or disturbs
the class before you come to class.
• You are responsible for all announcements made in class and on class web page. You are also
responsible for printing the assignments and lecture notes from the class web page.
• Attendance requirements. Minimum required attendance is %50 to lectures, %50 to recitations
and laboratory hours (each). Please note that although these are the minimum requirements outlined
for this course, it is best if you fully attend every hour. If you miss even a single hour, you will find
it hard to understand the following course/lab material since the topics are interrelated. Also note
that attendance-related Çankaya University regulations will be applied.
• Grading will be based on homework, case study, midterm and final exams. There will be three
homework assignments (each 10/3%, total 10%), a case study (10%), two midterm exams (each
25%, total 50%), and a final exam (30%); all exams will be closed book and closed notes). Letter
grades will be mainly based on the catalogue grading system described in Çankaya University
regulations. When grading, I will also pay attention to whether you:
– took both midterm exams (and surely the final exam if you are eligible to take it based on
regulations of the university),
– attended classes regularly and were actively involved in the class discussions,
– submitted and spent effort on assignments.
In order to avoid an NA grade, you need to: (1) satisfy attendance requirements, and (2) take
all midterm exams (or their makeup in accordance with the make-up policy), and the final exam.
• Homework assignments can be done in groups of size two at most. All homework assignments
will be collected at or prior to the assigned time on the assigned date. Late assignments will
NOT be accepted and you will get a credit of zero in case you do not submit the assignment on
time. Each student team is expected to submit their team’s work as a single assignment. Should
some submitted assignments be identical or be suspected to be identical to some other group(s)’
assignment, all involved parties will receive a credit of zero on the particular assignment. No extra
credit will be given for extra work and/or redoing assignments.
• Case Study. You will be assigned a case study that you will work on in groups. Details pertaining
to the case study will be provided later.
• Lab Hours. One hour of laboratory session will be held every week. You will have assignments in
some weeks (not graded) which you will work on during the lab sessions.
• Software Use. You need to download Gams (http://www.gams.com/download) and Lindo (http:
//www.lindo.com/downloads/lnd61.zip), which are the primary software packages we will use.
You may need to use other software which will be mentioned in class.
• Make-up exam will be given based on Çankaya University Regulations for unanticipated absences
and with valid excuse ONLY (e.g., illness with a doctor’s report). There is no makeup for the final
exam based on the Regulations.
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Course Topics. A tentative outline is given below and the instructor reserves the right to make changes
as she sees necessary. The preparatory reading from Lecture Notes should be completed before the corresponding class.
Week/Day
1
Feb 8
2
3
4
5
6
7
8
9
10
11
12
13
14
Reading
1-8
Topics
1. Introduction to Operations Research
Feb 10
9-12
2. Introduction to Linear Programming
Feb 15
13-17
Feb 17
19, 24
Feb 22
25-26
Feb 24
26-27, 30
Feb 29
31-32, 35
Mar 2
Mar 7
36-38
39
48
Mar 9
49-50
Mar 14
47, 51-52
Mar 16
54-56
Mar 21
59-62
3. Modeling Linear Programs:
Product-Mix Problem, Diet Problem
3. Modeling Linear Programs:
Static Workforce-Scheduling Problem & Extensions 1, 2
3. Modeling Linear Programs:
Static Workforce-Scheduling Problem Extensions 3, 4, 5
3. Modeling Linear Programs:
Static Workforce-Scheduling Problem Extensions 5, 6
3. Modeling Linear Programs:
Blending Problem
3. Modeling Linear Programs: Capital Budgeting Problem
4. Linear Multi-Period Decision Models: Production Sch.
4. Linear Multi-Period Decision Models:
Production Scheduling, Multiple Products
4. Linear Multi-Period Decision Models:
Backlogging
4. Linear Multi-Period Decision Models:
Lost Sales, Production Smoothing, Rolling Horizon
4. Linear Multi-Period Decision Models:
Financial Models, Dynamic Workforce Scheduling
5. Linear Programming Tricks
Mar 23
63-69
Mar 28
70-74
Mar 30
75-78
Apr 4
80-82
Apr 6
83-87
Apr 11
88-90
Apr 13
91-96
Apr 18
96-99
Apr 20
101-103
Apr 25
104-107
Apr 27
109-111
May 2
115-120
May 4
121-122
May 9
125-128
6. Introduction to Integer Programming:
Binary Variables
6. Introduction to Integer Programming:
Fixed Costs, Disjunctions
6. Introduction to Integer Programming:
Disjunctions, Assignment Problem
6. Introduction to Integer Programming:
Transportation Problem, Transshipment Problem
6. Introduction to Integer Programming:
Transshipment Problem, Set Covering Problem
6. Introduction to Integer Programming:
Cutting Stock Problem
7. Network Models:
Shortest Path Problem
7. Network Models:
Shortest Path Problem, Maximum Flow Problem
7. Network Models:
Minimum Cost Flow Problem
7. Network Models:
Minimum Spanning Tree Problem
7. Network Models:
Traveling Salesperson Problem
8. Introduction to MCDM:
Goal Programming
8. Introduction to MCDM:
Tradeoff Curves
9. Introduction to Nonlinear Programming
May 11
129-132
9. Introduction to Nonlinear Programming
3
Exam/
Assignment
HW 1 assigned
HW 1 due
Midterm 1
(Tentative)
HW 2 assigned
HW 2 due
Midterm 2
(Tentative)
HW 3 assigned
Case Study-I
(May 3)
Case Study-II due
HW 3 due (May 13)