Instructor:      Metin Türkay

Office:            ENG-205, x1586

E-Mail:           mturkay@ku.edu.tr e-mail for the course: indr262faq@ku.edu.tr

Lectures:   

Section 01: Mn&We 14:30-15:45 ENG-Z50

Section 02: Mn&We 16:00-17:15 ENG-Z50

PS:

A: Fr 16:00-17:15 ENG-120

B: Th 17:30-18:45 SNA-B152

C: We 17:30-18:45 CASE-B24

Lab:

A: Tu 17:30-18:45 SNA-B152

B: Mo 17:30-18:45 SNA-B152

C: Fr 08:30-09:45 SNA-B152

Office Hours: Mn&We 13:00-14:30, Tu 10:00-11:30

TAs:

Esma Bozgeyik (EB), ENG-212, indr262faq@ku.edu.tr

Soheil Derakhshan Nejad (SDN), ENG-212, indr262faq@ku.edu.tr

Sadra Shoarinejad (SS), ENG-212, indr262faq@ku.edu.tr

Emre Kaan Yılmaz (EKY), ENG-212, indr262faq@ku.edu.tr

Tugce Uzer (TU), ENG-212, indr262faq@ku.edu.tr

Hümeyra Akyüz (HA), ENG-212

Ezgi Kalemoglu (EK), ENG-212

Perinaz Alp (PA), ENG-212

Abdullah Izgi (AI), ENG-212

TA Office Hours:

EB: We 11:00-12:00

SDN: Th 14:00-15:00

SS: Th 16:00-17:00

EKY:

TU: We 08:30-10:00

HA: Fr 14:30-15:45

EK: Mo 16:30-17:30

PA: 

AI: Tu 16:00-17:30

KOLT Tutor:

Humeyra Akyuz: Mo 10:00-11:15, We: 11:30-12:45, Fr: 10:00-11:15

Brief Description

This course is designed to expose students to the concepts of modeling and optimization. The course emphasizes setting up optimization models from problem description and solving linear programming problems using the simplex method. The role of duality and sensitivity analysis for linear programming problems are examined. The range of problems that can be modeled and solved as linear programming problems are illustrated in engineering and management with computer implementations.

Course Goals

The students are expected to acquire the following skills in this course:

–  Develop a fundamental understanding of linear programming models,

–  Able to develop a linear programming model from problem description,

–  Apply the simplex method for solving linear programming problems,

–  Apply the revised simplex method to solve linear programming problems,

–  Express the dual of a linear programming problem and solve the resulting dual problem using the dual simplex method, interpret the results and obtain solution to the primal problem from the solution of the dual problem,

–  Conduct sensitivity analysis for linear programming problems and interpret the results,

–  Apply the Hungarian method for solving assignment problems,

–  Apply the transportation simplex method to solve transportation problems,

–  Demonstrate hands-on problem solving skills for linear programming problems using a Python and a variety of solvers.

Textbook

Hillier, F.S. and G.J. Lieberman, ‘Introduction to Operations Research’, 10th Ed., 2015, McGraw Hill, New York.

Computer Laboratory and Problem Sessions

This course has 4 credits and involves problems sessions and computer laboratory. The problem sessions cover basic problem solving skills and reinforce the material covered in the lectures. There may be quizzes related to homework questions during the problem sessions. The computer laboratory sessions cover basic skills in developing models and using solvers by using Python. You will apply these skills in the homework assignments.

Note: Failure to attend the PS and Computer Labs will result in an automatic F in the course. Students can miss at most 1 PS or Computer Labs in the semester with approved excuse.

Homework and Computer Exercises

Homework is assigned to expose students to exercise problems and understanding of the theory, and to evaluate their abilities and knowledge. Students should be prepared to spend considerable time for preparing homework. Students are expected to submit their homework before the due date and time. In addition, there are computer exercises for modeling and solution of mathematical programming problems. The aim of the computer exercises is to help students gain hands-on experience with state-of-the-art programming software, Python. The computer exercises must be submitted by the announced deadline. Each student is required to complete the computer exercises alone from start to end. Failing to submit homework and computer exercises on time will result in 25% grade reduction for each late day.

Exams

There are two midterm exams during the semester and a final exam at the end of the semester. Exams will be closed book and notes. No make-up exams will be given unless required by Koç University policies. Students who are eligible for a make-up exam due to official reasons must contact the instructor at least one week prior to the exam date. If you miss an exam due to unforeseen circumstances such as severe illness, death in the family, etc., you must submit a written and signed statement as soon as possible, stating why you were unable to take the exam, together with any supporting documentation such as a doctor’s medical report. Failure to comply with these instructions unconditionally leads to zero being awarded for the exam grade.

Course grade will be determined as follows:

Course Outline

Note: Topics to be covered and grade percentages may be modified by the course instructor.

Koç University Official Statement on Academic Honesty

Attendance Policy

All students are required to attend classes and discussion sessions such as tutorials, labs and problem sessions.  The course instructor will take attendance.

The students who fail to attend 1/3 of classes and discussion sessions may get an automatic F.

March 7, 2020 13:15
SNA-A52& SNA-B172, SNA-B173Mid Term I (Sample Exams: 2004, 2012)April 16, 2020SNA-A21& SNA-A22

Mid Term II (Sample Exams: 2004, 2012)

Failure to attend the PS and Computer Labs will result in an automatic F in the course. Students can miss at most 1 PS or Computer Lab in the semester with approved excuse.

Reading 1: Optimizasyon, Metin Türkay

Reading 2: George B. Dantzig: Operations research Icon, 2005, Operations research, vol. 53, 892-898

GAMS download link

Midterm I  (TBA) (Sample Exams: 2004, 2012)

Midterm II (TBA) (Sample Exams: 2004, 2012)

Final (TBA) (Sample Exams: 2004, 2012)