1. SECOND CYCLE - MASTER'S DEGREE
  2. THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
  3. MATHEMATICS DEPARTMENT
  4. 1441 Mathematics
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
Print

COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
MAT 639APPLIED MATHEMATICAL PROGRAMMING3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective The aim of this course is to develop a linear decision model for appropriate problems and to teach solution techniques for linear decision models.
Course Content Linear decision model, graphic and analytic solutions for linear programming problems, transportation model, duality, assignment model and network models, sensitivity analysis.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Defines mathematical programming problems.
2Defines basic concepts and principles of linear programming.
3Models to decision for the linear programming problem.
4Apply the Simplex algorithm.
5Solves the problem that crated by decision model.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08
LO 001        
LO 002        
LO 003        
LO 004        
LO 005        
Sub Total        
Contribution00000000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14798
Assignments155
Mid-terms11515
Final examination13535
Total Work Load

ECTS Credit of the Course





195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2023-2024 Spring1MUKADDES ÖKTEN TURACI


Print

Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
MAT 639 APPLIED MATHEMATICAL PROGRAMMING 3 + 0 1 Turkish 2023-2024 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Assoc. Prof. Dr. MUKADDES ÖKTEN TURACI moktenturaci@pau.edu.tr FEN A0316 %70
Goals The aim of this course is to develop a linear decision model for appropriate problems and to teach solution techniques for linear decision models.
Content Linear decision model, graphic and analytic solutions for linear programming problems, transportation model, duality, assignment model and network models, sensitivity analysis.
Topics
WeeksTopics
1 Notion of Linear Decision Model, Standart Form, Canonical Form.
2 Examples of Linear Programmnig.
3 Examples of Linear Programmnig.
4 General Structure of Linear Decision Model, Basic Operations and Concepts.
5 Fundamentals of Linear Programming and Solution Approaches.
6 Simplex Algorithm and Fundamentals of Simplex Algorithm.
7 Simplex Algorithm and Examples.
8 Big M Method.
9 Two-Phase Simplex Algorithm.
10 Duality in Linear Programming.
11 Sensitivity Analysis.
12 Transportation Models and Solution Algorithms.
13 Assignment Models and Solution Algorithms.
14 Network Models and Solution Algorithms.
Materials
Materials are not specified.
Resources
ResourcesResources Language
Kara, İ., "Doğrusal Programlama", Bilim Teknik Yayınevi,(2000)Türkçe
Winston, W.L., "Operations Research: Applications and Algorithms", Duxbury Press,(1994)English
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes