FACULTY OF BUSINESS

Department of Logistics Management

BA 250 | Course Introduction and Application Information

Course Name
Management Science
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
BA 250
Fall/Spring
3
0
3
6

Prerequisites
None
Course Language
English
Course Type
Service Course
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Discussion
Group Work
Problem Solving
Lecture / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives This course aims at teaching a variety of management science methods useful in solving management problems in many areas such as Marketing, Finance, and Production.
Learning Outcomes The students who succeeded in this course;
  • Describe basic concepts and techniques of mathematical modelling.
  • Formulate business problems mathematically.
  • Apply linear and integer programming for the solution of business problems.
  • Use computer software to solve complex mathematical models.
  • Analyze decision problems under both certainty and uncertainty.
Course Description The main emphasis of the course is how to model a managerial problem using mathematical modeling (Linear programming and Integer programming). Many examples from different application areas are given. Solution techniques for some special problems are discussed.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Break-even Point Analysis Chapter 1 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
2 Linear Programming: Modelling and Graphical Solution Chapter 2 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
3 Linear Programming: Modelling and Graphical Solution Chapter 2 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
4 Linear Programming: Sensitivity Analysis Chapter 3 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
5 Modelling Examples in Business Field Chapter 4 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
6 Modelling Examples in Business Field Chapter 4 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
7 Integer Linear Programming Models Chapter 5 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
8 Integer Linear Programming Models Chapter 5 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
9 Midterm Exam
10 Network Flow Models Chapter 6 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
11 Network Flow Models Chapter 6 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
12 Project Management Chapter 8 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
13 Project Management Chapter 8 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
14 Nonlinear Programming Chapter 10 (Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition)
15 Review of the Semester
16 Final Exam

 

Course Notes/Textbooks

Introduction to Management Science. Bernard W. Taylor III, Twelfth Edition, Prentice-Hall, New Jersey, ISBN-13: 978-0132752008

Suggested Readings/Materials

Operations Research Applications and Algorithms, Wayne L. Winston, Fourth Edition, Thomson Books, ISBN-13: 978-0534380588

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
5
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
20
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
35
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
3
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
16
2
32
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
1
15
15
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
36
36
Final Exam
1
40
40
    Total
171

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To be able to analyze complex problems in the field of logistics and supply chains

X
2

To be able to have good knowledge of sector related market leaders, professional organizations, and contemporary developments in the logistics sector and supply chains

X
3

To be able to participate in the sector-related communication networks and improve professional competencies within the business sector

X
4

To be able to use necessary software, information and communication technologies in the fields of logistics management and supply chain

X
5

To be able to understand and utilize the coordination mechanisms and supply chain integration

6

To be able to analyze the logistics and supply chain processes using the management science perspective and analytical approaches

7

To be able to design, plan and model in order to contribute to decision making within the scope of logistics and supply chains

X
8

To be able to interpret and evaluate the classical and contemporary theories in the field of logistics and supply chains

9

To be able to conduct projects and participate in teamwork in the field of logistics and supply chains

10

To be able to have an ethical perspective and social responsiveness when making and evaluating decisions.

X
11

To be able to collect data in the area of logistics and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1).

X
12

To be able to speak a second foreign at a medium level of fluency efficiently.

X
13

To be able to relate the knowledge accumulated throughout human history to their field of expertise.

X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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