TOROS ÜNİVERSİTESİ
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Course Information

OPTIMIZATION METHODS
Code	Semester	Theoretical	Practice	National Credit	ECTS Credit
		Hour / Week
CSE433	Fall	3	0	3	5

Prerequisites and co-requisites	None
Language of instruction	English
Type	Elective
Level of Course	Bachelor's
Lecturer	Asst. Prof. Omid SHARİFİ
Mode of Delivery	Face to Face
Suggested Subject	None
Professional practise ( internship )	None
Objectives of the Course	Mathematical Programming: Linear, Integer and Quadratic Programs - Linear Programming: Simplex and Dual Simplex Methods, Duality and Sensitivity Analysis, Extensions of Linear Programming - Integer Programming: Branch-and-Bound Algorithm, Cut Algorithms, The Transportation Algorithm, Scheduling Models - Non-Linear Programming: Single-Variable Optimization, Multivariable Optimization with & without Constraints - Dynamic Programming - Network Analysis: Minimum-span, Shortest-Route and Maximal-Flow Problems - Project Planning Using PERT/CPM - Inventory Models – Forecasting: Regression Methods, Smoothing Methods - Game Theory - Decision Theory - Markov Processes - Queuing Systems: M/M/1 Systems, M/M/s Systems, M/M/1/K Systems, M/M/s/K Systems
Contents of the Course	Mathematical Programming: Linear, Integer and Quadratic Programs - Linear Programming: Simplex and Dual Simplex Methods, Duality and Sensitivity Analysis, Extensions of Linear Programming - Integer Programming: Branch-and-Bound Algorithm, Cut Algorithms, The Transportation Algorithm, Scheduling Models - Non-Linear Programming: Single-Variable Optimization, Multivariable Optimization with & without Constraints - Dynamic Programming - Network Analysis: Minimum-span, Shortest-Route and Maximal-Flow Problems - Project Planning Using PERT/CPM - Inventory Models – Forecasting: Regression Methods, Smoothing Methods - Game Theory - Decision Theory - Markov Processes - Queuing Systems: M/M/1 Systems, M/M/s Systems, M/M/1/K Systems, M/M/s/K Systems

Learning Outcomes of Course

#	Learning Outcomes
1	Produce to solutions about engineering problems
2	Give information about optimization sciences
3	Produce to solutions about optimization scienes problems
4	Give information about last technologies of optimization sciences

Course Syllabus

#	Subjects	Teaching Methods and Technics
1	Mathematical Programming: Linear, Integer and Quadratic Programs	Lecture, discussion, presentation
2	logo ENTR  Search CONTACTHOMEPAGE TURKEY YALOVA YALOVA UNİVERSİTY PROGRAMS STUDENT ERASMUS DİPLOMA SUPPLEMENT CAMPUS BOLOGNA Programs First Cycle Transportatıon Engıneerıng Schedule 2014 - 2015	Lecture, discussion, presentation
3	Integer Programming: Branch-and-Bound Algorithm, Cut Algorithms, The Transportation Algorithm, Scheduling Models	Lecture, discussion, presentation
4	Non-Linear Programming: Single-Variable Optimization, Multivariable Optimization with & without Constraints	Lecture, discussion, presentation
5	Dynamic Programming	Lecture, discussion, presentation
6	Network Analysis: Minimum-span, Shortest-Route and Maximal-Flow Problems	Lecture, discussion, presentation
7	1.Midterm	Exam
8	Project Planning Using PERT/CPM - Inventory Models	Lecture, discussion, presentation
9	Forecasting: Regression Methods, Smoothing Methods	Lecture, discussion, presentation
10	Game Theory	Lecture, discussion, presentation
11	Decision Theory	Lecture, discussion, presentation
12	Markov Processes	Lecture, discussion, presentation
13	Queuing Systems: M/M/1 Systems, M/M/s Systems, M/M/1/K Systems, M/M/s/K Systems	Lecture, discussion, presentation
14	Final Exam	Exam
15
16

Course Syllabus

#	Material / Resources	Information About Resources	Reference / Recommended Resources

Method of Assessment

#	Weight	Work Type	Work Title
1	40%	Mid-Term Exam	Mid-Term Exam
2	60%	Final Exam	Final Exam

Relationship between Learning Outcomes of Course and Program Outcomes

#	Learning Outcomes	Program Outcomes	Method of Assessment
1	Produce to solutions about engineering problems	2͵3	1͵2
2	Give information about optimization sciences	2͵3	1͵2
3	Produce to solutions about optimization scienes problems	2͵3	1͵2
4	Give information about last technologies of optimization sciences	2͵3	1͵2

PS. The numbers, which are shown in the column Method of Assessment, presents the methods shown in the previous table, titled as Method of Assessment.

Work Load Details

#	Type of Work	Quantity	Time (Hour)	Work Load
1	Course Duration	14	3	42
2	Course Duration Except Class (Preliminary Study, Enhancement)	14	4	56
3	Presentation and Seminar Preparation	0	0	0
4	Web Research, Library and Archival Work	0	0	0
5	Document/Information Listing	0	0	0
6	Workshop	0	0	0
7	Preparation for Midterm Exam	0	0	0
8	Midterm Exam	1	1	1
9	Quiz	0	0	0
10	Homework	0	0	0
11	Midterm Project	0	0	0
12	Midterm Exercise	0	0	0
13	Final Project	1	20	20
14	Final Exercise	0	0	0
15	Preparation for Final Exam	0	0	0
16	Final Exam	1	1	1
				120