ECTS - Discrete Programming
Discrete Programming (IE506) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Discrete Programming | IE506 | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | N/A |
| Course Level | Ph.D. |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Problem Solving. |
| Course Lecturer(s) |
|
| Course Objectives | In this course, the students will be learning fundamental concepts of the discrete programming to be able to combine these concepts with their studies. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | 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
| 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 | 50 |
|---|---|
| Percentage of Final Work | 50 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
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 | |||||
ECTS/Workload Table
| 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 |
| Total Workload | 125 | ||