ISB221 Optimization Techniques - I

5 ECTS - 3-0 Duration (T+A)- 3. Semester- 3 National Credit

Information

Code ISB221
Name Optimization Techniques - I
Term 2024-2025 Academic Year
Semester 3. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 5 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Doç. Dr. NİMET ÖZBAY
Course Instructor Doç. Dr. NİMET ÖZBAY (A Group) (Ins. in Charge)


Course Goal / Objective

The aim of this course is to develop and solve a model for linear programming problems, to learn and solve transportation problems

Course Content

The content of this course consists of the topics of Definitions related to the Linear Programming Problem, Examples related to the Linear Programming Problem and Developing the Model, Hyperplanes, Convex Sets, Linear Functions on Convex Sets, Graphical Solution Method, Gauss Jordan Elimination, Linear Programming Problems in Canonical Form, Analytical Solution Method, The Simplex Method, Two Phase Method, Big M Method, Duality, Transportation Problems and Solution Methods

Course Precondition

None

Resources

-Elementary Linear Programing With Applications, Bernard Kolman and Robert E. Beck, Academic Press, 1980. -Doğrusal Programlama, Prof. Dr. İmdat Kara, Bilim Teknik Yayınevi, 1991. -Yöneylem Araştırması, Hamdy A. Taha (Çevirenler : Ş. Alp Baray-Şakir Esnaf), Literatür Yayıncılık, 2000. -Optimizasyon Teknikleri, Hasan Bal, Gazi Üniversitesi Yayınları, 1995. -İşletmede Sayısal Yöntemler ve Winqsb Uygulamaları, Prof. Dr. İsmail Erdem, Seçkin Yayıncılık, 2017.

Notes

-Optimizasyon, Ayşen Apaydın, A.Ü.F.F. Dön. Ser. Yayınları, 1996.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Describes the properties of the linear programming problem
LO02 Builds the linear programming model, solves this problem by graphical and analytical methods
LO03 Uses the simplex solution method
LO04 Distinguish the difference between the simplex and two phase methods
LO05 Uses the two phase method
LO06 Uses the Big M method
LO07 Write dual of the linear programming model
LO08 Solves balanced and unbalanced transportation models


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Explain the essence fundamentals and concepts in the field of Statistics
PLO02 Bilgi - Kuramsal, Olgusal Emphasize the importance of Statistics in life 4
PLO03 Bilgi - Kuramsal, Olgusal Define basic principles and concepts in the field of Law and Economics
PLO04 Bilgi - Kuramsal, Olgusal Produce numeric and statistical solutions in order to overcome the problems
PLO05 Bilgi - Kuramsal, Olgusal Use proper methods and techniques to gather and/or to arrange the data 2
PLO06 Bilgi - Kuramsal, Olgusal Utilize computer programs and builds models, solves problems, does analyses and comments about problems concerning randomization
PLO07 Bilgi - Kuramsal, Olgusal Apply the statistical analyze methods 4
PLO08 Bilgi - Kuramsal, Olgusal Make statistical inference (estimation, hypothesis tests etc.)
PLO09 Bilgi - Kuramsal, Olgusal Generate solutions for the problems in other disciplines by using statistical techniques and gain insight
PLO10 Bilgi - Kuramsal, Olgusal Discover the visual, database and web programming techniques and posses the ability of writing programs
PLO11 Beceriler - Bilişsel, Uygulamalı Distinguish the difference between the statistical methods
PLO12 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Make oral and visual presentation for the results of statistical methods 4
PLO13 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Have capability on effective and productive work in a group and individually 2
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 2
PLO15 Yetkinlikler - Öğrenme Yetkinliği Develop scientific and ethical values in the fields of statistics-and scientific data collection


Week Plan

Week Topic Preparation Methods
1 Definitions related to the Linear Programming Problem, Examples related to the Linear Programming Problem and Developing the Model Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
2 Hyperplanes, Convex Sets, Linear Functions on Convex Sets Source reading Öğretim Yöntemleri:
Anlatım, Tartışma, Problem Çözme
3 Graphical Solution Method Source reading Öğretim Yöntemleri:
Anlatım, Tartışma, Problem Çözme
4 Gauss Jordan Elimination, Linear Programming Problems in Canonical Form Source reading Öğretim Yöntemleri:
Anlatım, Tartışma
5 Analytical Solution Method Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
6 Simplex Solution Method Source reading Öğretim Yöntemleri:
Anlatım, Tartışma, Problem Çözme
7 Problem Solving-1 Source reading Öğretim Yöntemleri:
Problem Çözme, Tartışma
8 Mid-Term Exam Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav
9 Two Phase Method-1 Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
10 Two Phase Method-2 Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
11 Big M Method Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
12 The Dual of the Linear Programming Model Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
13 Balanced Transportation Model and Solution Methods-1 Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
14 Unbalanced Transportation Model and Solution Methods-2 Source reading Öğretim Yöntemleri:
Anlatım, Problem Çözme
15 Problem Solving-2 Source reading Öğretim Yöntemleri:
Anlatım, Tartışma, Problem Çözme
16 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Review the topics discussed in the lecture notes and sources Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) 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 12 12
Final Exam 1 18 18
Total Workload (Hour) 114
Total Workload / 25 (h) 4,56
ECTS 5 ECTS

Update Time: 04.07.2024 11:33