COURSE UNIT TITLE

: MANAGEMENT SCIENCE

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
QMT 2003 MANAGEMENT SCIENCE COMPULSORY 3 0 0 4

Offered By

BUSINESS ADMINISTRATION (English)

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR GÜZIN ÖZDAĞOĞLU

Offered to

BUSINESS ADMINISTRATION (English)

Course Objective

The course aims to provide students the basics of linear programming and management science tools for solving business-oriented optimization problems.

Learning Outcomes of the Course Unit

1   Have a knowledge understanding the basic concepts of mathematical models, linear programming methods
2   Be able to use essential tools of linear programming for making business decisions.
3   Employ critical thinking and independent problem-solving skills to optimize real world business problems.
4   Be able to use Spreadsheet Software and a solver to perform analysis and support presentations.
5   Communicate clearly the results of an basic optimization methods and explain the managerial implication.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to course Basic Concepts and Model Building
2 Basic Concepts and Model Building Introduction to Spreadsheet Modeling
3 Introduction to Optimization Modeling
4 Linear Programming Models
5 Linear Programming Models Sensitivity Analysis
6 Sensitivity Analysis
7 Minimization Exercises
8 Maximization Exercises
9 Linear Programming Models Business Applications
10 Network Models Assignment Problem
11 Network Models Assignment Problem
12 Network Models Assignment Problem
13 Network Models Assignment Problem
14 Integer Programming

Recomended or Required Reading

Text Books:
1. Management Science Modeling, Albright and Winston, 4th edition or later, South-Western Cengage Learning.
(Alternativly: Practical Management Science, Albright and Winston, 4th edition or later, South-Western Cengage Learning.)


Supplimentary Resources:
1. Işletme Problemleri için Optimizasyon -Adım Adım Uygulama Örnekleriyle, Sabri ERDEM, Makina Mühendisleri Odası Yayınları, 2010
2. Lecture slides complied by the instructor.
3. Public videos about electronic spreadsheets, linear programming and sensitivity analysis
4. Software: Spreadsheet Software with Solver add-in.

Planned Learning Activities and Teaching Methods

1. Lectures
Class lecture is highly interactive and format is direct. The instructor prompts students for response to questions
posed and solicits their thoughts on issues discussed. Lectures will focus on the transfer of basic optimization
concepts and techniques where comprehension is substantially enhanced by additional elaboration and
illustration. The emphasis is on the modeling of business problems and their solutions using computer software rather than rigorous mathematics.

2. Computer Applications

During the class hours, linear programming models of business problems specific to different fields will be constructed and spreadsheet and / or a solver software will be used to solve these models. Information on the use of these software platforms will be provided via applications associated with optimization problems during the class hours , sample videos, and books.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MT Midterm
2 PSL ProblemSolving
3 FN Final
4 FCG FINAL COURSE GRADE MT * 0.35 +PSL * 0.20 + FN * 0.45
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MT * 0.35 + PSL * 0.20 + RST * 0.45


Further Notes About Assessment Methods

Students will solve problems and report solutions that will be assigned as small homework in certain periods to show how much they understand the basic concepts of model building and analysis. This report will consist of two parts. The first part is the development of the algebraic model, the second part is the spreadsheet modeling and analysis. In this context, each homework will be evaluated over 100 points. The average of the scores obtained from these short assignments (PSL) will be added to the course grade with a weight of 20%.

Midterm exam (MT) will be carried out on a date to be announced in advance in the middle of the semester, and the final exam (FN) will be applied at the end of the semester in accordance with the determined schedule, these assessments will be added to the course grade with the weights stated above.

Assessment Criteria

Following criteria are considered to assess the assignments:

The student is able to clearly identify the problem (5 pts).
The student can organize the quantitative data given in the case study (5 pts).
The student is able to define appropriate set of variables (10 pts).
The student is able to formulate the objective function (10 pts).
The student is able to formulate the constraints (10 pts).
The student is able to write a complete mathematical model with respect to standard form (10 pts).
The student is able to select the technique which can solve the model (5 pts).
The student is able to formulate the model on a spreadsheet model (20 pts).
The student is able to solve the selected model using appropriate tools and technologies (e.g., optimization tools) (5 pts).
The student is able to interpret the outputs of the solution (20 pts).

Language of Instruction

English

Course Policies and Rules

Academic integrity is to demonstrate responsbile and honest behaviors and follow ethical principles in academia. All students should respect the intellectual property rights of others. Specifically every student should avoid plagiarism. All types of plagiarism are serious and violate academic integrity policy.

To understand and prevent plagiarism, please see the following link: https://www.plagiarism.org/understanding-plagiarism.

Contact Details for the Lecturer(s)

During the semester please use communication channels within online.deu.edu.tr platform such as meetings, messages, and chatroom.


Assoc.Prof.Dr.Güzin Özdağoğlu

guzin.kavrukkoca@deu.edu.tr

Office No at the Faculty: 132a





Office Hours

All communication in the scope of the course will be held within online.deu.edu.tr platform. If you need one-to-one support, you can write messages or request an appointment from the instructor or the teaching assisstant for a meeting (online).

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 11 2 22
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Final 1 1,5 2
Midterm 1 1,5 2
Practical exam 2 4 8
TOTAL WORKLOAD (hours) 106

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.111215222
LO.21314142
LO.311444143235
LO.411425145223
LO.511534134533