School/Faculty/Institute | Faculty of Engineering | |||||
Course Code | IE 305 | |||||
Course Title in English | Modeling and Methods in Optimization | |||||
Course Title in Turkish | Modelleme ve Optimizasyonda Yöntemler | |||||
Language of Instruction | EN | |||||
Type of Course | Select | |||||
Level of Course | Select | |||||
Semester | Fall | |||||
Contact Hours per Week |
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Estimated Student Workload | 151 hours per semester | |||||
Number of Credits | 6 ECTS | |||||
Grading Mode | Standard Letter Grade | |||||
Pre-requisites |
IE 202 - Operations Research I IE 202 - Operations Research I |
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Expected Prior Knowledge | None | |||||
Co-requisites | None | |||||
Registration Restrictions | None | |||||
Overall Educational Objective | ||||||
Course Description | ||||||
Course Description in Turkish |
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 heuristic algorithms for optimization problems; 4) design suitable heuristic 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; |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||||
2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||||
3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||||
4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||||
5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||||
6) Internalization and dissemination of professional ethical standards. | ||||||
7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||||
8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||||
9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||||
10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||||
11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||||
12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||||
13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
N None | S Supportive | H Highly Related |
Program Outcomes and Competences | Level | Assessed by | |
1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
6) | Internalization and dissemination of professional ethical standards. | N | |
7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
Prepared by and Date | HANDE KÜÇÜKAYDIN , |
Course Coordinator | HANDE KÜÇÜKAYDIN |
Semester | Fall |
Name of Instructor | Asst. Prof. Dr. HANDE KÜÇÜKAYDIN |
Week | Subject |
1) | Network Optimization Models: Shortest Path Problem, Minimal Spanning Tree Problem |
2) | Network Optimization Models: Maximum Flow Problem |
3) | Building LP Models |
4) | Interpreting and Using LP solutions |
5) | Interpreting and Using LP solutions, Combinatorial Optimization Problems |
6) | Computational Complexity, Heuristics for Combinatorial Optimization: Introduction, Reformulation, Rounding and Decomposition |
7) | Heuristics for Combinatorial Optimization: List-processing Heuristics, Neighborhoods and Neighbors, Steepest Descent |
8) | Heuristics for Combinatorial Optimization: List-processing Heuristics, Neighborhoods and Neighbors, Steepest Descent |
9) | Heuristics for Combinatorial Optimization: Simulated Annealing, Tabu Search, Genetic Algorithms |
10) | Non-linear Models in One Variable |
11) | Non-linear Models: Convexity and Unconstrained Optimization |
12) | Non-linear Models: Constrained Optimization |
13) | Integer Linear Models |
14) | Deterministic Dynamic Programming |
15) | Final Examination Period |
16) | Final Examination Period |
Required/Recommended Readings | Williams, H. P. (2013). Model Building in Mathematical Programming (5th Edition). Wiley Taha, H. A. (2011). Operations Research: An Introduction (9th Edition). Upper Saddle River, New Jersey: Pearson | ||||||||||||||||||
Teaching Methods | Lectures/contact hours using “flipped classroom” as an active learning technique | ||||||||||||||||||
Homework and Projects | 2 quizzes and 1 group project (groups of 2 students) | ||||||||||||||||||
Laboratory Work | None | ||||||||||||||||||
Computer Use | Visual Basic for Applications | ||||||||||||||||||
Other Activities | None | ||||||||||||||||||
Assessment Methods |
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Course Administration |
hande.kucukaydin@mef.edu.tr +902123953631 Instructor’s office and phone number: 5th Floor, (0212) 3953631 office hours: Tuesday 10:00-12:00 email address: hande.kucukaydin@mef.edu.tr Rules for attendance: Minimum attendance requirement is 70%. 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: Provided that proper documents of excuse are presented, a make-up exam will be given for each missed midterm. Missing a final: Faculty regulations. A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Statement on plagiarism: YÖK Regulations (http://3fcampus.mef.edu.tr/uploads/cms/webadmin.mef.edu.tr/4833_2.pdf ) |
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 | 1.5 | 77 | ||
Project | 1 | 5 | 20 | 25 | |||
Quiz(zes) | 2 | 4 | 0.5 | 9 | |||
Midterm(s) | 1 | 20 | 1.5 | 21.5 | |||
Final Examination | 1 | 25 | 2 | 27 | |||
Total Workload | 159.5 | ||||||
Total Workload/25 | 6.4 | ||||||
ECTS | 6 |