| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | İŞL214.4B2 | Multi-Purpose Decision Making II | Elective | 2 | 4 | 4 |
|
| Level of Course Unit |
| First Cycle |
| Objectives of the Course |
| 1. To analyze the differences of individual versus group decision making techniques 2. To teach when to use a group decision making approach 3. To teach different group decision making techniques under multiple criteria 4. To discuss their basic drawbacks, similarities and differences |
| Name of Lecturer(s) |
| Arş. Gör. Kübra ÖZCAN AKYOL |
| Learning Outcomes |
| 1 | Student defines decision problems ans creates mathematical models. | | 2 | Student will be able to solve potential problems in business decision making processes | | 3 | Student uses mathematical techniques to solve decision problems and makes decision analysis on the solutions. | | 4 | Student defines basic objective functions for creation multi-objective models. | | 5 | Student applies the results of analysis of the models to real-life problems. |
|
| Mode of Delivery |
| Normal Education |
| Prerequisites and co-requisities |
| None |
| Recommended Optional Programme Components |
| None |
| Course Contents |
| To introduce the students to various methods of enhancing creativity and group decision-making under multiple criteria. To analyse the various phases and stages of group decision making, the context, the scope the similarities and the differences, the breadth and the depth of GDM processes and techniques using hands-on learning techniques as much as possible and practicable. To integrate the theory and practice through articles based on the real life application of the GDM methods. |
| Weekly Detailed Course Contents |
|
| 1 | Introduction to Multi-objective Optimization | | | | 2 | Linear Programming with Single Objective | | | | 3 | Single vs Multi-objective Optimization | | | | 4 | Multi-objective Optimization Problem | | | | 5 | Dominance and Pareto Optimality | | | | 6 | Classical Methods for solving MOOP | | | | 7 | Evolutionary Algorithms for solving MOOP | | | | 8 | Midterm | | | | 9 | Decision Aid and Preference Modelling | | | | 10 | MCDM: Scoring Methods | | | | 11 | MCDM: Scoring Methods | | | | 12 | MCDM: Outranking Methods | | | | 13 | MCDM: Outranking Methods | | | | 14 | Utility Theory | | | | 15 | Utility Theory | | | | 16 | Final Exam | | |
|
| Recommended or Required Reading |
| Yöneylem Araştırması (6. Basımdan Çeviri), Hamdy A. Taha, Literatür Yayıncılık, İstanbul, 2000.
Doğan, İ. (1994). Yöneylem araştırması teknikleri ve işletme uygulamaları. Marmara Üniversitesi İktisadi ve İdari Bilimler Fakültesi. |
| Planned Learning Activities and Teaching Methods |
|
| Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
|
| Workload Calculation |
|
| Midterm Examination | 1 | 30 | 30 |
| Final Examination | 1 | 30 | 30 |
| Makeup Examination | 1 | 30 | 30 |
| Attending Lectures | 1 | 32 | 32 |
|
| Contribution of Learning Outcomes to Programme Outcomes |
| LO1 | 2 | 1 | 2 | 3 | 3 | 1 | 3 | 3 | 2 | 1 | | LO2 | 2 | 4 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | | LO3 | 3 | 3 | 3 | 3 | 3 | 2 | 3 | 4 | 2 | 3 | | LO4 | 3 | 1 | 2 | 3 | 1 | 4 | 3 | 4 | 2 | 1 | | LO5 | 5 | 3 | 4 | 5 | 3 | 4 | 3 | 4 | 3 | 4 |
|
| * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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