| School/Faculty/Institute | Faculty of Engineering | ||||||
| Course Code | IE 307 | ||||||
| Course Title in English | Modeling and Methods in Optimization | ||||||
| Course Title in Turkish | Modelleme ve Optimizasyonda Yöntemler | ||||||
| Language of Instruction | EN | ||||||
| Type of Course | Ters-yüz öğrenme,Laboratory Work,Lecture | ||||||
| Level of Course | İleri | ||||||
| Semester | Fall | ||||||
| Contact Hours per Week |
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| Estimated Student Workload | 175 hours per semester | ||||||
| Number of Credits | 7 ECTS | ||||||
| Grading Mode | Standard Letter Grade | ||||||
| Pre-requisites |
IE 202 - Operations Research I |
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| Co-requisites | None | ||||||
| Expected Prior Knowledge | Prior knowledge in deterministic operations research methodologies | ||||||
| Registration Restrictions | none | ||||||
| Overall Educational Objective | To learn mathematical modeling in depth and use/develop solution methods. | ||||||
| Course Description | This course presents various aspects of mathematical modeling and of problem-solving strategies used for the solution of realistic, large-scale, complex problems. The following topics are covered: modeling with integer programming; branch-and-bound method; building advanced linear programming models; importance of linearity; linearization of some nonlinear programming problems; goal programming; shortest path problem; minimal spanning tree problem; maximum flow problem; examples of combinatorial optimization problems; heuristics for combinatorial optimization problems such as list processing heuristics, local search; non-linear models in one variable; convexity; unconstrained and constrained optimization in non-linear programming. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) identify network structure in optimization problems and use suitable solution methods; 2) formulate good mathematical models; 3) explore optimization algorithms; 4) design suitable algorithms for optimization problems, analyze and interpret the results, and draw conclusions; 5) give a demonstration of a designed algorithm; 6) function effectively as a member of a team; 7) apply solution techniques for non-linear problems. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| 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 |
| 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 |
| 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 |
| 3) | An ability to communicate effectively with a range of audiences | H | Proje |
| 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 | H | Proje |
| 6) | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | H | Exam,Proje |
| 7) | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies | S | Proje |
| Prepared by and Date | HANDE KÜÇÜKAYDIN , March 2024 |
| Course Coordinator | HANDE KÜÇÜKAYDIN |
| Semester | Fall |
| Name of Instructor | Asst. Prof. Dr. HANDE KÜÇÜKAYDIN |
| Week | Subject |
| 1) | Modeling with Integer Programming |
| 2) | Modeling with Integer Programming |
| 3) | Modeling with Integer Programming, Branch-and-Bound |
| 4) | Branch-and-Bound, Advanced LP Models |
| 5) | Advanced LP Models, Network Optimization Models: Shortest Path Problem, Minimal Spanning Tree Problem |
| 6) | Network Optimization Models: Shortest Path Problem, Minimal Spanning Tree Problem, Maximum Flow Problem |
| 7) | Network Optimization Models: Maximum Flow Problem, Combinatorial Optimization Problems |
| 8) | Heuristics for Combinatorial Optimization: Introduction, Reformulation, Rounding and Decomposition, List-processing Heuristics |
| 9) | Heuristics for Combinatorial Optimization: List-processing Heuristics, Neighborhoods and Neighbors |
| 10) | Neighborhoods and Neighbors, Local Search |
| 11) | Local Search, Non-linear Models in One Variable |
| 12) | Non-linear Models in One Variable, Non-linear Models: Convexity and Unconstrained Optimization |
| 13) | Non-linear Models: Convexity and Unconstrained Optimization, Non-linear Models: Constrained Optimization |
| 14) | Non-linear Models: Constrained Optimization |
| 15) | Final Exam/Project/Presentation period |
| 16) | Final Exam/Project/Presentation period |
| Required/Recommended Readings | • Williams, H. P. (2013). Model Building in Mathematical Programming (5th Edition). Wiley • Hillier, F.S., Lieberman, G. J. (2015). Introduction to Operations Research (10th Edition). McGraw-Hill Education | |||||||||||||||
| Teaching Methods | Lectures/contact hours using “flipped classroom” as an active learning technique | |||||||||||||||
| Homework and Projects | A project will be completed in groups of students. | |||||||||||||||
| Laboratory Work | None | |||||||||||||||
| Computer Use | Yes | |||||||||||||||
| Other Activities | none | |||||||||||||||
| Assessment Methods |
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| Course Administration |
hande.kucukaydin@mef.edu.tr 212 3953631 Instructor’s -office and phone number: 5th floor, 212 3953631 -office hours: TBA -email address: hande.kucukaydin@mef.edu.tr Exams and quizzes: Closed book and closed notes. 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: Provided that proper documents of excuse are presented, a make-up exam will be given for each missed quiz. Missing a project: Project deadlines are always extendable up to 72 hours, with submissions late for (0,24] hours receive 70% of the credit they get, (24,48] hours receive 35% , and (48,72] receive 10%. 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, a make-up exam will be given for each missed midterm. 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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| 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 | 4 | 1 | 84 | ||
| Project | 1 | 40 | 2 | 42 | |||
| Quiz(zes) | 3 | 6 | 1 | 21 | |||
| Midterm(s) | 1 | 25 | 3 | 28 | |||
| Total Workload | 175 | ||||||
| Total Workload/25 | 7.0 | ||||||
| ECTS | 7 | ||||||