At the end of this course, the students; 1) Will be able to explain the decision-making process. 2) Understands the importance and applications of Decision Support Systems. 3) Solves complex decision problems. 4) Will learn the models involved in analysis. 5) Can define Decision Support Systems (DSS) terminologies.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
NA
COURSE DEFINITION
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction to Decision Making
2nd Week
Introduction to Quantitative Decision Making
3rd Week
Linear Programming (Mathematical Programming)
4th Week
Microsoft Excel and VBA Programming
5th Week
Game Theory
6th Week
Decision Trees
7th Week
Simulation and its applications
8th Week
Mid-term examination
9th Week
Forecasting Techniques
10th Week
Forecasting Techniques
11th Week
Expert Systems
12th Week
Artificial Neural Networks
13th Week
Artificial Neural Networks
14th Week
Evolutionary Optimization
RECOMENDED OR REQUIRED READING
Vicki L. Sauter, Decision Support Systems for Business Intelligence, 2nd Edition, Wiley Decision Making, Stephen P. Fitzgerald, Capstone Publishing, 2002 Operations Management, Roberta S. Russel, Bernard W. Taylor, Prentice Hall, 1998
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Lecture,Discussion,Problem Solving,Other
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
40
Total(%)
40
Contribution of In-term Studies to Overall Grade(%)
40
Contribution of Final Examination to Overall Grade(%)
60
Total(%)
100
ECTS WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
1,5
1,5
Preparation for Quiz
Individual or group work
14
3
42
Preparation for Final exam
1
25
25
Course hours
14
3
42
Preparation for Midterm exam
1
20
20
Laboratory (including preparation)
4
2
8
Final exam
1
2,5
2,5
Homework
4
1
4
Total Workload
145
Total Workload / 30
4,83
ECTS Credits of the Course
5
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
