MANAGEMENT ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
ENM4117 Quantitative Decision Making Spring 3 0 3 6
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. SAİT GÜL
Recommended Optional Program Components: None
Course Objectives: 1. Develop necessary skills for problem formulation, setting goals and parameters of the problem.
2. Develop an understanding about choosing the most approprite model, translating it into a software, and justify alternative solutions of the
3. Improve data analysing skills.
4. Make students familiar with decision making software packages.

Learning Outcomes

The students who have succeeded in this course;
1. Solve real world problems having conflicting criteria (in general).
2. Develop a mathematical model for real-world problems.
3. Select the most appropriate method to solve the problem.
4. Be familiar with some of the most widely used decision support software packages.
5. Comment on the the outputs of these software and draw concluions.
6. Manage the group decision making process.

Course Content

This course includes topics such as single person or group decision making especially in multi-criteria problems. Students will practise structuring and modelling decision making problems, together with deciding on the most appropriate method and/or tool to solve them. Forecasting, optimization, multi-criteria decision making and data analysis are some of the topics to be covered.

Teaching Methods: Case Study, Project, Lecture, Reading, Implementation, Problem Solving

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Syllabus Review Introduction to Quantitative Analysis
2) Introduction to Multiple Criteria Decision-Making: Structuring & Constructing the Decision Model
3) Analyzing the Decision Model via AHP
4) Analyzing the Decision Model via Elementary Methods, SAW, WP, TOPSIS
5) Analyzing the Decision Model via PROMETHEE
6) Analyzing the Decision Model via Hybrid Methods
7) Mid-Term Exam
8) Decision Analysis under Uncertainty
9) Decision Analysis under Risk
10) Decision Trees
11) Decision Trees with Bayesian Analysis
12) Utility Theory
13) Game Theory
14) Term Project Presentations

Sources

Course Notes / Textbooks: • Render, B., Stair, R.M., Hanna, M.E., Hale, T.S. Quantitative Analysis for Management, 13th Ed., Pearson, 2018.
• Winston, W.L., Operations Research: Applications and Algorithms, Thomson – Brooks/Cole, 2004.
• Yoon, K.P., Hwang, C.L., Multi-Attribute Decision Making: An Introduction, Sage University Papers Series, London, 1995.
• Tzeng, G.H., Huang, J.J., Multiple Attribute Decision Making: Methods and Applications, CRC Press, 2011.
References: • Render, B., Stair, R.M., Hanna, M.E., Hale, T.S. Quantitative Analysis for Management, 13th Ed., Pearson, 2018.
• Winston, W.L., Operations Research: Applications and Algorithms, Thomson – Brooks/Cole, 2004.
• Yoon, K.P., Hwang, C.L., Multi-Attribute Decision Making: An Introduction, Sage University Papers Series, London, 1995.
• Tzeng, G.H., Huang, J.J., Multiple Attribute Decision Making: Methods and Applications, CRC Press, 2011.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Project 1 % 35
Midterms 1 % 25
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 25
PERCENTAGE OF FINAL WORK % 75
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 2 28
Project 1 50 50
Midterms 1 15 15
Final 1 25 25
Total Workload 160

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Build up a body of knowledge in mathematics, science and engineering subjects; use theoretical and applied information in these areas to model and solve engineering problems.
2) identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose.
3) Design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.)
4) Devise, select, and use modern techniques and tools needed for engineering management practice; employ information technologies effectively.
5) Design and conduct experiments, collect data, analyze and interpret results for investigating engineering management problems.
6) Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently.
7) Demonstrate effective communication skills in both oral and written English and Turkish.
8) Recognize the need for lifelong learning; show ability to access information, to follow developments in science and technology, and to continuously educate him/herself.
9) Develop an awareness of professional and ethical responsibility.
10) Know business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
11) Know contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions.
12) Develop effective and efficient managerial skills.