Information Package / Course Catalogue
| Computer Engineering | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Engineering | |||||
| Course Code | IE 301 | |||||
| Course Title in English | Operations Research II | |||||
| Course Title in Turkish | Yöneylem Araştırması II | |||||
| Language of Instruction | EN | |||||
| Type of Course | Flipped Classroom,Lecture | |||||
| Level of Course | Introductory | |||||
| Semester | Fall | |||||
| Contact Hours per Week |
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| Estimated Student Workload | 148 hours per semester | |||||
| Number of Credits | 6 ECTS | |||||
| Grading Mode | Standard Letter Grade | |||||
| Pre-requisites |
MATH 221 - Probability and Statistics for Engineering I | MATH 227 - Probability and Statistics for Engineering I |
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| Expected Prior Knowledge | Basic probability knowledge | |||||
| Co-requisites | None | |||||
| Registration Restrictions | none | |||||
| Overall Educational Objective | To learn stochastic operations research methodologies. | |||||
| Course Description | This course introduces the students to stochastic models and methodologies for analyzing and providing solutions to decision-making problems with uncertainties. The course will emphasize Markov Chains, Exponential Distribution, Poisson Process, Queuing Theory and Markov Decision Process, and their applications in real life problems. | |||||
| Course Description in Turkish | Bu dersin belirsizlik altında karar verme problemlerinde kullanılmak üzere rassal metot ve yöntemleri öğrencilere tanıtır. Bu ders Markov Zincirleri, Üssel Dağılım, Poisson Süreçleri, Kuyruk Teorisi ve Markov Karar Süreçleri konularını ve bunların uygulamalarını içerecektir. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) show the ability to construct stochastic models of the problems that arise in random environments using Markov Chains; 2) construct models using Poisson Process; 3) perform performance evaluation using Queuing Theory; 4) evaluate different strategies using Markov Decision Process. 5) applies stochastic modeling and queuing theory techniques on some real life cases 6) analyzes, compares alternative stochastic optimization techniques and designs a solution method to stochastic optimization problems |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| 1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | ||||||
| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | ||||||
| 3) An ability to communicate effectively with a range of audiences | ||||||
| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | ||||||
| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | ||||||
| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | ||||||
| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | H | Exam,HW |
| 2) | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | S | Exam,HW |
| 3) | An ability to communicate effectively with a range of audiences | N | |
| 4) | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | N | |
| 5) | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | N | |
| 6) | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | S | Exam,HW |
| 7) | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. | N |
| Prepared by and Date | EVREN GÜNEY , December 2023 |
| Course Coordinator | EVREN GÜNEY |
| Semester | Fall |
| Name of Instructor | Assoc. Prof. Dr. EVREN GÜNEY |
Course Contents
| Week | Subject |
| 1) | Introduction to Stochastic Processes – Markov Chains |
| 2) | Illustrative examples of Markov Chain Applications |
| 3) | Steady State Probabilities and Applications |
| 4) | Absorbing Markov Chains and Their Analysis (Quiz 1) |
| 5) | Application of Markov Chains |
| 6) | Exponential Distribution (ED) and Poisson Processes (PP) I |
| 7) | Exponential Distribution (ED) and Poisson Processes (PP) II |
| 8) | Introduction to Queuing Theory – Terminology |
| 9) | M/M/1/GD/N/ type of Queuing Systems (Midterm) |
| 10) | M/M/1/GD/N type of Queuing Systems |
| 11) | Application of Queuing Systems |
| 12) | Markov Decision Process I |
| 13) | Markov Decision Process II |
| 14) | Markov Decision Process III |
| 15) | Final examination / presentation period |
| 16) | Final examination / presentation period |
| Required/Recommended Readings | Textbook: Taha, H. A., Operations Research: An Introduction (9th Edition). Upper Saddle River, New Jersey: Pearson. Supplementary: (1) Ross, S.M., Introduction to Probability Models (8th Edition). Academic Press, Elsevier. (2) Winston, W. L., Operations Research – Applications and Algorithms. Brooks/Cole CENGAGE Learning, Belmont, Canada. | |||||||||||||||
| Teaching Methods | Lectures/contact hours using “flipped classroom” as an active learning technique | |||||||||||||||
| Homework and Projects | • Problems from textbook (they will not be collected and not graded, quiz questions will be very similar or identical to the problems). • A term project that covers all topics learned in this course | |||||||||||||||
| Laboratory Work | none | |||||||||||||||
| Computer Use | Excel use is strongly recommended | |||||||||||||||
| Other Activities | none | |||||||||||||||
| Assessment Methods |
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| Course Administration |
guneye@mef.edu.tr 212 3953740 Instructor’s -office and phone number: 5th floor, 212 3953740 -office hours: TBA -email address: semra.agrali@mef.edu.tr Exams and quizzes: Closed book and closed notes. Homework: N/A Rules for attendance: YÖK regulations. You are responsible for the announcements made in class. Rules for late submission of assignments: N/A Missing a quiz: No make-up will be given for the missed quizzes. For certain excuses (decided by the instructor) the percentage of the missed quiz may be added to the midterm or to the final. Missing a midterm: You are expected to be present without exception and to plan any travel around these dates accordingly. Medical emergencies are of course excluded if accompanied by a doctor’s note. A note indicating that you were seen at the health center on the day of the exam is not a sufficient documentation of medically excused absence from the exam. The note must say that you were medically unable to take the exam. Provided that proper documents of excuse are presented, missed midterm by the student will be given the grade of the final exam. No make-up will be given. If you fail to take the exam on the assigned day and do not have a valid excuse, you will be given zero (0) on the exam. Employment interviews, employer events, weddings, vacations, etc. are not excused absences. Eligibility to take the final exam: YÖK regulations. Missing a final: Faculty regulations. A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Academic dishonesty and plagiarism: YÖK Regulations |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 3 | 2 | 84 | ||
| Homework Assignments | 5 | 1.5 | 0.5 | 10 | |||
| Quiz(zes) | 5 | 1.5 | 0.5 | 10 | |||
| Midterm(s) | 1 | 20 | 2 | 22 | |||
| Final Examination | 1 | 25 | 2 | 27 | |||
| Total Workload | 153 | ||||||
| Total Workload/25 | 6.1 | ||||||
| ECTS | 6 | ||||||