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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS ELECTIVE

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR FEHMI BURÇIN ÖZSOYDAN

Offered to

Industrial Engineering

Course Objective

The aim of this course is to teach students to model and analyze industrial engineering systems using various mathematical tools. In this context, this course aims to convey to students the relationship between basic linear algebra, mathematical models and their components, possible solutions of the models and optimization concepts, and their equivalents in production systems.

Learning Outcomes of the Course Unit

1   To be able to understand the basic topics of linear algebra. 2   Being able to understand the issues of linear independence and identifying possible solutions. 3   To understand the relationship between linear systems, linear mathematical models and linear algebra. 4   Ability to design and analyze a linear and discrete system and mathematical model. 5   To be able to understand the theory of representation, various representation problems and the theories and proofs on these subjects.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to systems, models and linear algebra: Matrices and vectors 2 Matrix and vector operations 3 Linear equation systems and solution approaches 4 Concept of linear independence and evaluation of possible solutions 5 Introduction to mathematical modeling: Mathematical representation of a system expressed verbally and the concepts of set, index, decision variable, parameter, constraint and objective function. 6 Mathematical modeling, system design applications and the concept of optimization 7 Mathematical modeling, system design applications and the concept of optimization 8 Midterm 9 Concept of modeling and optimization in discrete solution spaces 10 Concept of modeling and optimization in discrete solution spaces 11 Introduction to graph theory: Basic components of graphs 12 Various applications in graph theory 13 Theories and proofs related to various problems in graph theory 14 In class study

Recomended or Required Reading

Wayne L. Winston, Operations Research: Applications and Algorithms, Thomson

Planned Learning Activities and Teaching Methods

Course will be taught in the class

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 PRJ PROJECT 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + PRJ * 0.20 + FIN * 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + PRJ * 0.20 + RST * 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm %30 + Project %20 + Final %50

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

burcin.ozsoydan@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparation for final exam 1 24 24 Preparations before/after weekly lectures 13 2 26 Preparation for midterm exam 1 7 7 Preparing assignments 1 7 7 Final 1 2 2 Midterm 1 2 2 Project Assignment 1 2 2 TOTAL WORKLOAD (hours) 109

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 LO.1 5 LO.2 5 LO.3 5 LO.4 5 LO.5 5