# Linear Programming (IE502) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Linear Programming IE502 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English Elective Courses Ph.D. Face To Face Lecture, Problem Solving. Asst. Prof. Dr. Babek Erdebilli In this course, the students will be learning the fundamental concepts of linear programming in order to utilize it for their specific problems. The students who succeeded in this course; Acquaintance of students with the fundamental concepts of linear programming. Ability of students to develop an insight about the role of linear programming for different engineering disciplines. As a consequence it is planned to improve students' Geometric and theorical aspects of Linear optimization theory of the Simplex algorithm. Simplex algorithm, linear programming, duality theory and economic interpretations, the simplex, big-m, two-phase, revised simplex, the dual simplex methods, sensitivity and post-optimality analysis, special forms of linear programming problems and their solution methods.

### Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Optimization: Linear optimization, mathematical basis, modeling and xamples.
2 Optimization: Linear optimization, mathematical basis, modeling and xamples.
3 Vector space, matrices, system of simultaneous linear equations.
4 Convex sets and convex functions, polyhedral sets.
5 Simplex method: extreme points and optimality, basdic feasible soltions.
6 Simplex method: a key to simplex method, geometric motivation, and its algebra.
7 Starting solution and termination: basic feasible solutions.
8 Midterm exam
9 Starting solution and termination: special cases.
10 Special simplex implementations.
11 Optimality condition on linear programming
12 Duality: formulations and primal-dual relationships.
13 Post-optimality analysis: dual-simplex method
14 Post-optimality analysis: parametrical analysis.
15 Students' projects presentations
16 Students' projects presentations

### Sources

Course Book 1. Linear and non Linear Optimization Igor Griva, Stephen G.Nash, Ariela Sofer

### Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation 6 10
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 40
Final Exam/Final Jury 1 50
Toplam 8 100
Percentage of Semester Work 100 100

### Course Category

Core Courses X

### The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Ability to carry out advanced research activities, both individual and as a member of a team
2 Ability to evaluate research topics and comment with scientific reasoning
3 Ability to initiate and create new methodologies, implement them on novel research areas and topics
4 Ability to produce experimental and/or analytical data in systematic manner, discuss and evaluate data to lead scintific conclusions
5 Ability to apply scientific philosophy on analysis, modelling and design of engineering systems
6 Ability to synthesis available knowledge on his/her domain to initiate, to carry, complete and present novel research at international level
7 Contribute scientific and technological advancements on engineering domain of his/her interest area
8 Contribute industrial and scientific advancements to improve the society through research activities

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 1 16
Presentation/Seminar Prepration
Project 1 4 4
Report
Homework Assignments 4 4 16
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 16 16
Prepration of Final Exams/Final Jury 1 25 25