Main Page     Information on the Institution     Degree Programs     General Information for Students     Türkçe  

 DEGREE PROGRAMS


 Associate's Degree (Short Cycle)


 Bachelor’s Degree (First Cycle)


 Master’s Degree (Second Cycle)

  Course Description
Course Name : Linear Programming

Course Code : MT 469

Course Type : Optional

Level of Course : First Cycle

Year of Study : 4

Course Semester : Fall (16 Weeks)

ECTS : 5

Name of Lecturer(s) : Assoc.Prof.Dr. AHMET TEMİZYÜREK

Learning Outcomes of the Course : Describes the properties of the linear programming problem
Creates a linear programming model and Solves the problem using solution methods such as graphical and analytical methods
Uses the Simplex Solution technique
Is aware of the diference between the Simplex method and the two-phase method
Uses the Two-Phase Method
Uses Big M Method
Is able to write dual of a linear model, distinguishes the Relations between the solutions of Main problems and Dual problems.
Is able to write Balanced and unbalanced transportation model and solves them.
Solves the Graph and assignment models.

Mode of Delivery : Face-to-Face

Prerequisites and Co-Prerequisites : None

Recommended Optional Programme Components : None

Aim(s) of Course : To establish a model for Linear Programming Problems and to be able to solve the established problems with various momethods. To teach applications of linear programming in various areas.

Course Contents : Introduction to linear programming and examples. Approaches for solutions of a linear programming problem: graphical approach, analytic approach. The simplex Method: the use of artificial variables, the two stage simplex algorithm. Duality, the theory of the dual simplex Method and its applications. The transportation problem and solution of a transportation problem. The relationship between game theory and linear programming.

Language of Instruction : Turkish

Work Place : Classrooms Faculty of Arts and Sciences


  Course Outline /Schedule (Weekly) Planned Learning Activities
Week Subject Student's Preliminary Work Learning Activities and Teaching Methods
1 Definitions and examples about Linear programming problems Review of the relevant pages from sources lectures and problem-solving
2 Hyperplanes, Convex sets and linear functions over the convex sets Review of the relevant pages from sources lectures and problem-solving
3 Geometric solutions Review of the relevant pages from sources lectures and problem-solving
4 Gauss-Jordan reduction, Linear Programming Problems in Canonical form Review of the relevant pages from sources lectures and problem-solving
5 Analytical solution Review of the relevant pages from sources lectures and problem-solving
6 The Simplex Method Review of the relevant pages from sources lectures and problem-solving
7 Two-Phase Method Review of the relevant pages from sources lectures and problem-solving
8 mid term exam Review the topics discussed in the lecture notes and sources Written exam
9 Two-Phase Method and The Big M Method Review of the relevant pages from sources lectures and problem-solving
10 The dual of the linear model, Relationships between Solutions of the original and dual models. Review of the relevant pages from sources lectures and problem-solving
11 Transportation models and solution techniques Review of the relevant pages from sources lectures and problem-solving
12 Assignment model and Hungarian algorithm Review of the relevant pages from sources lectures and problem-solving
13 Graph models and shortest route problems Review of the relevant pages from sources lectures and problem-solving
14 Maximal flow problems Review of the relevant pages from sources lectures and problem-solving
15 Maximal flow problems Review of the relevant pages from sources lectures and problem-solving
16/17 Final exam Review the topics discussed in the lecture notes and sources Written exam


  Required Course Resources
Resource Type Resource Name
Recommended Course Material(s)  Optimizasyon, Ayşen Apaydın,A.Ü.F.F. press, 1996
 Yöneylem Araştırması, Ahmet Öztürk, Ekin publishing house,2009
Required Course Material(s)   Elementary Linear Programing With Applications, Bernard Kolman and Robert E. Beck, Academic Press,1980


  Assessment Methods and Assessment Criteria
Semester/Year Assessments Number Contribution Percentage
    Mid-term Exams (Written, Oral, etc.) 1 100
    Homeworks/Projects/Others 0 0
Total 100
Rate of Semester/Year Assessments to Success 40
 
Final Assessments 100
Rate of Final Assessments to Success 60
Total 100

  Contribution of the Course to Key Learning Outcomes
# Key Learning Outcome Contribution*
1 Is able to prove Mathematical facts encountered in secondary school. 4
2 Recognizes the importance of basic notions in Algebra, Analysis and Topology 1
3 Develops maturity of mathematical reasoning and writes and develops mathematical proofs. 3
4 Is able to express basic theories of mathematics properly and correctly both written and verbally 5
5 Recognizes the relationship between different areas of Mathematics and ties between Mathematics and other disciplines. 3
6 Expresses clearly the relationship between objects while constructing a model 4
7 Draws mathematical models such as formulas, graphs and tables and explains them 2
8 Is able to mathematically reorganize, analyze and model problems encountered. 3
9 Knows at least one computer programming language 2
10 Uses effective scientific methods and appropriate technologies to solve problems 0
11 Knows programming techniques and is able to write a computer program 0
12 Is able to do mathematics both individually and in a group. 0
13 Has sufficient knowledge of foreign language to be able to understand Mathematical concepts and communicate with other mathematicians 0
14 In addition to professional skills, the student improves his/her skills in other areas of his/her choice such as in scientific, cultural, artistic and social fields 0
* Contribution levels are between 0 (not) and 5 (maximum).

  Student Workload - ECTS
Works Number Time (Hour) Total Workload (Hour)
Course Related Works
    Class Time (Exam weeks are excluded) 14 3 42
    Out of Class Study (Preliminary Work, Practice) 14 3 42
Assesment Related Works
    Homeworks, Projects, Others 0 0 0
    Mid-term Exams (Written, Oral, etc.) 1 15 15
    Final Exam 1 20 20
Total Workload: 119
Total Workload / 25 (h): 4.76
ECTS Credit: 5