Information Package / Course Catalogue
IE 307 Modeling and Methods in Optimization
MEF University
Degree Programs
Psychology
General Information For Students
Diploma SupplementErasmus Policy Statement
Psychology
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 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 Flipped Classroom,Laboratory Work,Lecture
Level of Course Advanced
Semester Fall
Contact Hours per Week
Lecture: 4 Recitation: none Lab: none Other: none
Estimated Student Workload 175 hours per semester
Number of Credits 7 ECTS
Grading Mode Standard Letter Grade
Pre-requisites IE 202 - Operations Research I
Expected Prior Knowledge Prior knowledge in deterministic operations research methodologies
Co-requisites None
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 Description in Turkish Bu ders, matematiksel modellemenin çeşitli yönlerini ve gerçeğe uygun, büyük boyutlu, karmaşık problemlerin çözümü için kullanılan problem çözme stratejilerini tanıtır. Ders boyunca en kısa yol problemi; tam sayılı programlama ile modelleme; dal-sınır yöntemi ; ileri düzey doğrusal programlama modelleri kurma; doğrusallığın önemi; bazı doğrusal olmayan problemlerin doğrusallaştırılması; hedef programlama; minimum örten ağaç problemi; en büyük akış problemi; kombinatoryal optimizasyon problem örnekleri; açgözlü sezgiseller yerel arama gibi kombinatoryal problemler için sezgiseller; tek değişkenli doğrusal olmayan modeller; dışbükeylik; doğrusal olmayan programlamada kısıtsız ve kısıtlı optimizasyon konuları işlenir.

Course Learning Outcomes and Competences

Upon 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) 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.

Relation to Program Outcomes and Competences

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 , March 2024
Course Coordinator HANDE KÜÇÜKAYDIN
Semester Fall
Name of Instructor Asst. Prof. Dr. HANDE KÜÇÜKAYDIN

Course Contents

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 MethodsLectures/contact hours using “flipped classroom” as an active learning technique
Homework and ProjectsA project will be completed in groups of students.
Laboratory WorkNone
Computer UseYes
Other Activitiesnone
Assessment Methods
Assessment Tools Count Weight
Laboratory 7 % 0
Quiz(zes) 2 % 10
Project 1 % 30
Midterm(s) 2 % 60
TOTAL % 100
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

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 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