# Discrete Programming (IE506) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Discrete Programming IE506 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 fundamental concepts of the discrete programming to be able to combine these concepts with their studies. The students who succeeded in this course; Acquaintance of students with the fundamental concepts of Discrete programming. Ability of students to develop an insight about the role of Discrete programming for different engineering disciplines. The theory, computation, and application of deterministic models; integer programming, mixed integer programming, zero-one programming, knapsack problems, cutting planes and polyhedral approach, branch and bound methods, Lagrangean relaxation, heuristics, decision analysis and games, nonlinear integer programming.

### Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Optimization: Linear optimization, mathematical basis, modeling and examples.
2 Optimization: Linear optimization, mathematical basis, modeling and examples.
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: artifical starting solutions..
10 Starting solution and termination: special cases.
11 Special simplex implementations.
12 Optimality condition on linear programming
13 Duality: formulations and primal-dual relationships.
14 Post-optimality analysis: dual-simplex method
15 Post-optimality analysis: parametrical analysis.
16 Final Examination Period

### Sources

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

### Evaluation System

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 50 50 100

### 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