TÜRKÇE
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Information
| Unit | FACULTY OF ENGINEERING |
| INDUSTRIAL ENGINEERING PR. | |
| Code | ENM362 |
| Name | Stochastic Models |
| Term | 2018-2019 Academic Year |
| Semester | 6. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 4 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MELİK KOYUNCU |
| Course Instructor |
Prof. Dr. MELİK KOYUNCU
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
To develop the operations research knowledge and skills by using stochastic model techniques
Course Content
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
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Gain the use of statistical distribution |
| LO02 | Can comment the use of statistical distributions |
| LO03 | Can model the Markov Models |
| LO04 | Gain the use of Markov Models |
| LO05 | Can model the queing systems |
| LO06 | Can use the queing systems at the production systems |
| LO07 | Can solve the analytic solutions of queing networks |
| LO08 | Can apply the queing networks at the production systems |
| LO09 | Can compare the altrenative solutions by using queing systems |
| LO10 | Can make the economic analysis by using queing systems |
| LO11 | Can model the Markov decision process |
| LO12 | Can use the Markov decision process |
| LO13 | Can apply the queing systems |
| LO14 | Can apply the queing systems |
| LO15 | Can apply the queing systems |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Has sufficient background on topics related to mathematics, physical sciences and industrial engineering. | 5 |
| PLO02 | - | Gains ability to use the acquired theoretical knowledge on basic sciences and industrial engineering for describing, formulating and solving an industrial engineering problem, and to choose appropriate analytical and modeling methods. | 5 |
| PLO03 | - | Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems . | 5 |
| PLO04 | - | Gains ability to choose and apply methods and tools for industrial engineering applications. | 5 |
| PLO05 | - | Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions. | 5 |
| PLO06 | - | Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. | 5 |
| PLO07 | - | Can access information and to search/use databases and other sources for information gathering. | 4 |
| PLO08 | - | Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. | 4 |
| PLO09 | - | Can use computer software in industrial engineering along with information and communication technologies. | 4 |
| PLO10 | - | Can use oral and written communication efficiently. | 4 |
| PLO11 | - | Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession. | 4 |
| PLO12 | - | Has a conscious understanding of professional and ethical responsibilities. | 4 |
| PLO13 | - | Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. | 2 |
| PLO14 | - | Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | The role of probability and statistics at the Stochastic models | Reading the related chapter from the textbook | |
| 2 | Analyzing data by statistics | Reading the related chapter from the textbook | |
| 3 | Discrete probability distributions (Bernoulli,Geometric ,Poisson etc.) | Reading the related chapter from the textbook | |
| 4 | Continous probability distributions (Normal, Lognormal, Weibull, Beta etc.) | Reading the related chapter from the textbook | |
| 5 | Introduction to Markov Chains | Reading the related chapter from the textbook | |
| 6 | transition probabilities | Reading the related chapter from the textbook | |
| 7 | First passage times | Reading the related chapter from the textbook | |
| 8 | Mid-Term Exam | Classical exam | |
| 9 | Absorbing states | Reading the related chapter from the textbook | |
| 10 | Introduction to queuing theory | Reading the related chapter from the textbook | |
| 11 | Modelling the interarrival and service times | Reading the related chapter from the textbook | |
| 12 | Distribution fitting techniques | Reading the related chapter from the textbook | |
| 13 | M/M/1 , M/M/s queuing models | Reading the related chapter from the textbook | |
| 14 | The queues have finite calling population and other queing models | Reading the related chapter from the textbook | |
| 15 | Jackson queuing networks and Application of queuing models at the modern manufacturing systems | Reading the related chapter from the textbook | |
| 16 | Term Exams | Reading the related chapter from the textbook and classical exam | |
| 17 | Term Exams | Reading the related chapter from the textbook and classical exam |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
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 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 13 | 13 |
| Final Exam | 1 | 17 | 17 |
| Total Workload (Hour) | 100 | ||
| Total Workload / 25 (h) | 4,00 | ||
| ECTS | 4 ECTS | ||