Syllabus ( BUS 657 )
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Basic information
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| Course title: |
Computer Aided Quantitative Decision Making Techniques |
| Course code: |
BUS 657 |
| Lecturer: |
Prof. Dr. Hakan KİTAPÇI
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| ECTS credits: |
7.5 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
1/2, Fall and Spring |
| Level of course: |
Second Cycle (Master's) |
| Type of course: |
Area Elective
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| Language of instruction: |
Turkish
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
none |
| Professional practice: |
No |
| Purpose of the course: |
To have the students informed and gained skills about how to apply advanced quantitative techniques in computer to their business problems to be able to give right and speedy decisions. |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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This course offers managers how to use quantative decision making techniques in order to take rational decision making, and optimize production and services activities effectively
Contribution to Program Outcomes
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Define and manipulate advanced concepts and common terminology of social sciences
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Critically review and contribute to the literature related to his/her area of expertise
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Formulate and solve advanced management problems concerning the issues of competitive strategy, investment and project management.
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Collect theoretical information about contemporary management issues and evaluate real cases in the light of them
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Identify the relationship between marketing management and consumer behaviors.
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Gain detailed information about strategic, tactical and operational dimensions of business management, analyze and design the business/company in organizational level
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Work effectively in multi-disciplinary research teams
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Develop an awareness of continuous learning in relation with modern technology
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Effectively express his/her research ideas and findings both orally and in writing
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Defend research outcomes at seminars and conferences.
Method of assessment
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Written exam
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Oral exam
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Homework assignment
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Seminar/presentation
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Contents
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| Week 1: |
Linear programming, Integer programming. |
| Week 2: |
Linear goal programming, Integer goal programming. |
| Week 3: |
Quadratic programming, Integer quadratic programming. |
| Week 4: |
Network models. |
| Week 5: |
Nonlinear programming. Project. |
| Week 6: |
Dynamic programming. |
| Week 7: |
Project programming and aplication. Midterm exam |
| Week 8: |
Queing analysis, queing system simulation. |
| Week 9: |
Inventory theory and systems. |
| Week 10: |
Forecasting models, Decision analysis. |
| Week 11: |
Markov process. |
| Week 12: |
Quality control charts. |
| Week 13: |
Job scheduling. |
| Week 14: |
Facility location and layout, Material requirement planning. |
| Week 15*: |
... |
| Week 16*: |
Presentation of Projects. Final exam |
| Textbooks and materials: |
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| Recommended readings: |
KAZAN, Halim., Bilgisayar Destekli Kantitatif Karar Verme Teknikleri (Yayınlanmamış Ders Notları),
Kantitatif Karar Verme Teknikleri Yöneylem Araştırması Osman Halaç ALFA / İşletme-İktisat-Muhasebe Dizisi
Behçet Altaylı: Simülasyon Kullanımı ile Sistem İyileştirme. Hava Kuvvetleri Komutanlığı 1994
Applied Operations Research: Examples from Defense Assessment by R. W. Shephard, Plenum, 1988.
Yönetim Kararlarında Kantitatif Yöntemler - Yöneylem Araştırması Dr. Behçet Altaylı; Isbn: 975-96141-0-3; 605 Sayfa, Ciltli, Lüx Baskı) |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
7 |
20 |
| Other in-term studies: |
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0 |
| Project: |
5 |
20 |
| Homework: |
10 |
20 |
| Quiz: |
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0 |
| Final exam: |
16 |
40 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
3 |
14 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
6 |
10 |
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| Term project: |
10 |
2 |
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| Term project presentation: |
1 |
1 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
10 |
1 |
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| Mid-term: |
1 |
1 |
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| Personal studies for final exam: |
10 |
1 |
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| Final exam: |
2 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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