Faculty Of Engıneerıng
Industrıal Engıneerıng (Englısh)
Course Information
| OPERATIONS RESEARCH III |
| Code |
Semester |
Theoretical |
Practice |
National Credit |
ECTS Credit |
| Hour / Week |
| INE310 |
Spring |
2 |
2 |
3 |
5 |
| Prerequisites and co-requisites |
|
| Language of instruction |
English |
| Type |
Required |
| Level of Course |
Bachelor's |
| Lecturer |
Assit. Prof. Dr. Melik KOYUNCU |
| Mode of Delivery |
Face to Face |
| Suggested Subject |
NONE |
| Professional practise ( internship ) |
None |
| Objectives of the Course |
To develop the operations research knowledge and skills by using stochastic model techniques |
| Contents of the Course |
Basic statistical concepts, Introduction to queuing systems, M/M/1,M/M/s and the other queue models,queuing networks, Markov chains and its applications
|
Learning Outcomes of Course
| # |
Learning Outcomes |
| 1 |
Student shall gain knowledge on optimisation concept
|
| 2 |
Student will be able to model the real life problems
|
| 3 |
Student will be able to model inventory, network and queuing models.
|
| 4 |
|
Course Syllabus
| # |
Subjects |
Teaching Methods and Technics |
| 1 |
Markov Chains, Transition Probabilities, n-step Transition Probabilities |
Lecturing |
| 2 |
Markov Chains and some examples |
Lecturing |
| 3 |
Classification of States, Steady States |
Lecturing |
| 4 |
Average First Passage Times |
Lecturing |
| 5 |
Markov Chains |
Lecturing |
| 6 |
Markov Chain Examples |
Lecturing |
| 7 |
Midterm |
Exam |
| 8 |
Queuing Theory |
Lecturing |
| 9 |
Queuing Theory-Application |
Lecturing |
| 10 |
Network Models-1 |
Lecturing |
| 11 |
Network Models-2CPM |
Lecturing |
| 12 |
Network Models-2PERT |
Lecturing |
| 13 |
Inventory Models-1 |
Lecturing |
| 14 |
Inventory Models-2 |
Lecturing |
| 15 |
Review |
Lecturing |
| 16 |
Final Exam |
|
Course Syllabus
| # |
Material / Resources |
Information About Resources |
Reference / Recommended Resources |
| 1 |
R.L. Rardin, Optimization in Operations Research, Pearson Education. |
|
|
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 |
Student shall gain knowledge on optimisation concept
|
1 |
1͵2 |
| 2 |
Student will be able to model the real life problems
|
2 |
1͵2 |
| 3 |
Student will be able to model inventory, network and queuing models.
|
4 |
1͵2 |
| 4 |
|
|
|
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 |
4 |
56 |
| 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 |
1 |
6 |
6 |
| 8 |
Midterm Exam |
1 |
2 |
2 |
| 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 |
0 |
0 |
0 |
| 14 |
Final Exercise |
0 |
0 |
0 |
| 15 |
Preparation for Final Exam |
1 |
14 |
14 |
| 16 |
Final Exam |
1 |
2 |
2 |
| |
136 |
