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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| INDUSTRIAL ENGINEERING (PhD) | |
| Code | EM016 |
| Name | Stochastic Processes |
| Term | 2019-2020 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MELİK KOYUNCU |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
To develop the operationsresearch 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 itsapplications
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 | Gain the use of MarkovModels |
| LO06 | Can use the queing systems at the production systems |
| LO07 | Can use the queing systems at the production systems |
| LO08 | Can compare the altrenative solutions by using queing systems |
| LO09 | Can compare the altrenative solutions by using queing systems |
| LO10 | Can compare the altrenative solutions by using queing systems |
| LO11 | Can compare the altrenative solutions by using queing systems |
| LO12 | Can apply the queing systems |
| LO13 | Can apply the queing systems |
| LO14 | Can apply the queing systems |
| LO15 | Can apply the queuing networks |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Understands, interprets and applies knowledge in his/her field domain both in-depth and in-breadth by doing scientific research in industrial engineering. | 5 |
| PLO02 | - | Acquires comprehensive knowledge about methods and tools of industrial engineering and their limitations. | 5 |
| PLO03 | - | Designs and performs analytical modeling and experimental research and analyze/solves complex matters emerged in this process. | 5 |
| PLO04 | - | Completes and applies the knowledge by using scarce and limited resources in a scientific way and integrates the knowledge into various disciplines. | 5 |
| PLO05 | - | Keeps up with the recent changes and applications in the field of Industrial Engineering and examines and learns these innovations when necessary. | 5 |
| PLO06 | - | Has the ability to propose new and/or original ideas and methods, develops innovative solutions for designing systems, components or processes. | 5 |
| PLO07 | - | Develops original definitions that will provide innovation to the field at the level of expertise for current and advanced information in the field based on graduate qualifications. | 5 |
| PLO08 | - | Designs Industrial Engineering problems, develops innovative methods to solve the problems and applies them. | 5 |
| PLO09 | - | Works in multi-disciplinary teams and takes a leading role and responsibility. | 5 |
| PLO10 | - | Identifies, gathers and uses necessary information and data. | 5 |
| PLO11 | - | Follows, studies and learns new and developing applications of industrial engineering. | 4 |
| PLO12 | - | Uses a foreign language in verbal and written communication at least B2 level of European Language Portfolio. | 2 |
| PLO13 | - | Presents his/her research findings systematically and clearly in oral and written forms in national and international platforms. | 5 |
| PLO14 | - | Understands social and environmental implications of engineering practice. | 5 |
| PLO15 | - | Considers social, scientific and ethical values in the process of data collection, interpretation and announcement of the findings. | 5 |
| PLO16 | - | Works in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. | 5 |
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 bynstatistics | Reading the related chapter from the textbook | |
| 3 | Discrete probability distributions (Bernoulli,Geometric ,Poisson etc.) | Reading the related chapter from the textbook | |
| 4 | Discrete probability distributions(Bernoulli,Geometric ,Poisson etc.) | Reading the related chapter from the textbook | |
| 5 | Introduction to Markov Chains | Reading the related chapter from the textbook | |
| 6 | Introduction to Markov Chains | 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 | Absorbing states | Reading the related chapter from the textbook | |
| 11 | Modelling the interarrivaland 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 networksand Application of queuing modelsat the modern manufacturing systems | Reading the related chapter from the textbook | |
| 16 | Term Exams | Classical exam | |
| 17 | Term Exams | Classical exam |