Course Details

MATHEMATICAL MODELING

IE211

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
3IE211MATHEMATICAL MODELING3+2+047

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program INDUSTRIAL ENGINEERING
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course To abstract a real-world system/problem conceptually
To develop mathematical models that are appropriate for the system/problem
To solve a mathematical model by using available off-the-shelf software (e.g., GAMS, CPLEX, EXCEL SOLVER, EXPRESS, GUROBI)
To interpret the solutions obtained from the models in terms of the real-world system.
Course Content Being able to solve the real-life problems and obtaining the right solution requires understanding and modeling the problem correctly and applying appropriate optimization tools and skills to solve the mathematical model. This course will focus on how to formulate, analyze, and solve mathematical models that represent real-world problems. In this course, how to use optimization software for solving optimization problems will be discussed. In particular, this course will cover linear programming, nonlinear programming, problem definition and formulation, sensitivity analysis, network optimization, integer linear programming, big-M method, and integrality property.
Course Methods and Techniques Lectures following the course material, student participation in problem-solving sessions, group work within the class, assignments, quizzes, midterm exams, final exams, and an ongoing real-life project throughout the semester.
Prerequisites and co-requisities ( MATH151 )
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Betül Kayışoğlu betul.kayisoglu@agu.edu.tr
Asist Prof.Dr. Betül Çoban betul.coban@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources - Operations Research: Applications and Algorithms by Wayne L. Winston, 4th Edition, Thomson Brooks/Cole, 2004.
Will be published on Canvas
Will be published on Canvas
Will be published on Canvas
Will be published on Canvas

Course Category
Mathematics and Basic Sciences %15
Engineering %60
Engineering Design %25
Social Sciences %0
Education %0
Science %0
Health %0
Field %0

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı 1 % 6
Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı 1 % 24
Quiz/Küçük Sınav 5 % 15
Ödev 2 % 10
Final examination 1 % 30
Diğer (Staj vb.) 1 % 15
Total
11
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Yazılı Sınav 1 3 3
Grup Projesi 1 50 50
Sunum için Hazırlık 2 1 2
Proje 1 50 50
Kişisel Çalışma 14 2 28
Derse Devam 14 5 70
Final Sınavı 1 3 3
Total Work Load   Number of ECTS Credits 7 206

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Formulate decision problems as linear, integer, nonlinear, network flow, and multi-objective models with optimization software.
2 Differentiate between convex and non-convex optimization problems and the general search technique in solving optimization problems
3 Solve linear programming problems by means of the graphical approach, primal and dual simplex methods
4 Interpret sensitivity analysis results for linear programming
5 Code an optimization model and/or algorithms using Excel, GAMS optimization software, Java or other modeling languages and interpret the solution.
6 Design a team project to solve a real-world problem and share the results of a real-world problem related team project (written and orally) with peers in a meaningful and professional manner.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Modelling, Syllabus Discussion Lecture Slides Will be published on Canvas
2 Mathematical Modeling in General Lecture Slides, Textbook Will be published on Canvas
3 Linear Programming Models Lecture Slides, Textbook Will be published on Canvas
4 Integer Programming Models Lecture Slides, Textbook Will be published on Canvas
5 Graphical Solution of Linear Programming Models Will be published on Canvas
6 Sensitivity Analysis Will be published on Canvas
7 Duality and Sensitivity Analysis Will be published on Canvas
8 Fall Break Will be published on Canvas
9 Project Progress Presentations Will be published on Canvas
10 Modeling Techniques Will be published on Canvas
11 Modeling Techniques Will be published on Canvas
12 Modeling Techniques Will be published on Canvas
13 Modeling Techniques Will be published on Canvas
14 Network Optimization Will be published on Canvas
15 Project Final Presentations Will be published on Canvas


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
C1 3 3 3 3 2 1 1
C2 3 1 3 2
C3 1 2 1
C4 1 1 1 2 3
C5 1 1 1 3 2
C6 3 3 3 3 3 3 3 2 2 3 1 1

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=71653&lang=en
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