DECISION MAKING IN ENGINEERING AND TECHNOLOGY MANAGEMENT

MT506

Course Code: MT506
Semester: -
Theory+Practice: 3 + 0
ECTS: 10

Course Type

Elective Course

Course Level

Master's Degree With Thesis

Year of Study

Semester

ECTS

Name of Lecturer(s)

Associate Professor Barış Keçeci

Learning Outcomes

At the end of this course, the students;

Understand advanced scientific and mathematical principles and apply them to the practice of multi criteria/objective decision making
Gain an ability to use advanced computer programs as tools for multi criteria/objective decision making applications
Gain an ability to apply the advanced principles of data analysis, and design of a selection problem
Gain an ability to apply the design process to multi objective decision making problems, including the consideration of different technical alternatives while bearing in mind cost, environmental concerns, safety, and other constraints
Gain an ability to analyze, measure, test, and evaluate a selection problem

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

None

Course Description

Course Contents

WEEK	TOPICS
1^st Week	Decision problem and the basic concepts.,
2^nd Week	Decision problem and the basic concepts.
3^rd Week	modeling, basic concepts of linear programming
4^th Week	modeling, basic concepts of linear programming
5^th Week	Simplex Algorithm and sensitivity analysis
6^th Week	Transportation and assignment problems
7^th Week	Transportation and assignment problems
8^th Week	Midterm exam
9^th Week	Nonlinear programming
10^th Week	Nonlinear programming
11^th Week	Integer programming.
12^th Week	Integer programming
13^th Week	Dynamic programming.
14^th Week	Dynamic programming.

Recommended or Required Reading

(1)Chen, S-J, Hwang C-L. Fuzzy Multible Attribute Decision Making-Methods and Applications, Springer-Verlag, 1992.
(2)Lai, Y-J, Hwang C-L. Fuzzy Multible Objective Decision Making-Methods and Applications, Springer-Verlag, 1996.
(3)Wayne L. Winston, Operations Research. Applications and Algorithms, Third
edition, Duxbury Press, Belmont, California, 1994,
(4)Ronald R. Rardin (Purdue University), Optimization in Operations Research,
Prentice-Hall, Inc., Upper Saddle River, New Jersey, 1998.

Planned Learning Activities and Teaching Methods

Lecture
Questions/Answers
Problem Solving
Practice
Presentation

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	30
Assignment	1	15
Quiz	1	10
Attendance	1	5
Total(%)		60
Contribution of In-term Studies to Overall Grade(%)		60
Contribution of Final Examination to Overall Grade(%)		40
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	2	2
Preparation for Quiz	1	14	14
Individual or group work	14	13	182
Preparation for Final exam	1	25	25
Course hours	14	3	42
Preparation for Midterm exam	1	25	25
Laboratory (including preparation)
Final exam	1	2	2
Homework	1	14	14
Total Work Load			306
Total Work Load / 30 (h)			10.2
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4	LO5
K1	X	X	X	X	X
K2	X	X	X	X	X
K3		X		X
K4			X
K5
K6
K7
K8
K9
K10
K11