Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| OPERATIONS RESEARCH | - | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Esra BAYTÖREN |
| Name of Lecturer(s) | Prof.Dr. Kazım SARI |
| Assistant(s) | |
| Aim | Students are aimed to have the necessary qualifications and background to be able to formulate and solve simple business decision problems using operations research techniques. |
| Course Content | This course contains; Operations Research – Introduction,Introduction to Optimization,Linear Programming – Building Models with Linear Programming,Linear Programming – Solution Concept,Linear Programming – Sensitivity Analysis,Linear Programming – The Simplex Method,Integer and Binary Integer Linear Programming,Linear Goal Programming,Network Models - Scope, Definition and Applications,Network Models – Minimal Spanning Tree Algorithms,Network Models - Shortest Path Algorithms,Network Models – Project Management (Pert and CPM),Deterministic Dynamic Programming,Simulation Models. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Will be able to explain the purpose of operations research. | 12, 13, 16, 6, 9 | A, D, E |
| 1.1 Lists the usage areas of operations research | ||
| 1.2 Categorizes the operations research methods | ||
| 1.3 Defines the concept of modeling | ||
| 2. Will be able to explain the logic of optimization. | 12, 13, 16, 6, 9 | A, D, E |
| 2.1 Explains the difference between linear and nonlinear optimization models | ||
| 2.2 Explains the concepts of local optimum and global optimum | ||
| 2.3 Explains constrained and unconstrained optimization concepts | ||
| 3. Will be able to explain linear programming models. | 12, 13, 16, 6, 9 | A, D, E |
| 3.1 Composes linear programming models of simple business problems | ||
| 3.2 Developes integer and binary integer linear programming models | ||
| 3.3 Creates logical constraints using binary variables | ||
| 4. Will be able to solve linear programming models. | 12, 13, 16, 6, 9 | A, D, E |
| 4.1 Explains the logic of graphical solution | ||
| 4.2 Recognizes degenerate cases in linear programming models | ||
| 4.3 Applies the Simplex method | ||
| 4.4 Explains the solution logic of integer linear programming models | ||
| 5. Will be able to design goal programming models. | 12, 13, 16, 6, 9 | A, D, E |
| 5.1 Explains the difference between object and goal | ||
| 5.2 Explains the difference between weighted and priority goal programming | ||
| 5.3 Solves goal programming models using MS Excel Solver | ||
| 6. Will be able to explain the relations among network models and business applications. | 12, 13, 16, 6, 9 | A, D, E |
| 6.1 Designs the network in which the total distance is minimized with minimum spanning tree algorithms | ||
| 6.2 Calculates the shortest path on the network using Shortest Path algorithms | ||
| 6.3 Describes how projects are planned, monitored and controlled using PERT and CPM algorithms | ||
| 7. Will be able to explain the essentials of the simulation. | 12, 13, 16, 6, 9 | A, D, E |
| 7.1 Explains simulation types | ||
| 7.2 Explains Monte Carlo Simulation | ||
| 7.3 Applies simulation in spreadsheets (MS Excel) |
| Teaching Methods: | 12: Problem Solving Method, 13: Case Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Operations Research – Introduction | |
| 2 | Introduction to Optimization | |
| 3 | Linear Programming – Building Models with Linear Programming | |
| 4 | Linear Programming – Solution Concept | |
| 5 | Linear Programming – Sensitivity Analysis | |
| 6 | Linear Programming – The Simplex Method | |
| 7 | Integer and Binary Integer Linear Programming | |
| 8 | Linear Goal Programming | |
| 9 | Network Models - Scope, Definition and Applications | |
| 10 | Network Models – Minimal Spanning Tree Algorithms | |
| 11 | Network Models - Shortest Path Algorithms | |
| 12 | Network Models – Project Management (Pert and CPM) | |
| 13 | Deterministic Dynamic Programming | |
| 14 | Simulation Models |
| Resources |
| [1] Operations Research: An Introduction, Hamdy A. Taha, 11th edition, Pearson, 2023 [2] Introduction to Operations Research, Hillier, F.S. & Lieberman, G.J., 9th Edition, McGrawHill, 2010 |
| [3] Yöneylem Araştırması, Taha, Hamdi A., 6.Basımdan çeviri, Çeviri: Ş.A.Baray ve Ş.Esnaf, Literatür Yayıncılık, 2016 [4] Yöneylem Araştırması: Nicel Karar Teknikleri, Özkan, Şule, 3. Baskı, Nobel Yayıncılık, 2012 [5] Lecture notes will be available at http://mebis.medipol.edu.tr |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında teorik bilgileri tanımlar. | ||||||
| 2 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında gerekli matematiksel ve istatistiki yöntemleri anlatır. | ||||||
| 3 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında gerekli en az bir bilgisayar programını kullanır. | ||||||
| 4 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında gerekli olan mesleki yabancı dil yeterliliğini gösterir. | ||||||
| 5 | Alanına dair projeler hazırlar ve takım çalışmalarını yönetir. | ||||||
| 6 | Mesleki alanda yaşam boyu öğrenmenin bilinciyle uluslararası lojistik ve tedarik zinciri yönetimi alanında gelişmeleri izleyerek kendini sürekli yenileyip edindiği bilgi ve becerileri eleştirel olarak değerlendirir. | ||||||
| 7 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında teorik ve uygulamaya yönelik bilgileri kullanır. | ||||||
| 8 | En az A1 düzeyinde bir yabancı dili kullanarak güncel teknolojileri takip eder, sözlü/yazılı iletişim kurar. | ||||||
| 9 | Örgüt/kurumsal, iş ve toplumsal etik değerlerini benimser ve kullanır. | ||||||
| 10 | Topluma hizmet duyarlılığı çerçevesinde, sosyal sorumluluk ilkelerini benimser ve gerektiğinde inisiyatif alır. | ||||||
| 11 | Disiplinler arası çalışmalar yürütebilmek için farklı disiplinlerde temel bilgileri ve verileri analiz ederek alanında kullanır. | ||||||
| 12 | Uluslararası lojistik ve tedarik zinciri yönetimi sektörlerindeki sorunlar karşısında hem mikro hem makro çerçevede uygun öneriler sunar. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 3 | 30 | 90 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 0 | 0 | 0 | |||
| General Exam | 1 | 48 | 48 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 180 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(180/30) | 6 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| OPERATIONS RESEARCH | - | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Esra BAYTÖREN |
| Name of Lecturer(s) | Prof.Dr. Kazım SARI |
| Assistant(s) | |
| Aim | Students are aimed to have the necessary qualifications and background to be able to formulate and solve simple business decision problems using operations research techniques. |
| Course Content | This course contains; Operations Research – Introduction,Introduction to Optimization,Linear Programming – Building Models with Linear Programming,Linear Programming – Solution Concept,Linear Programming – Sensitivity Analysis,Linear Programming – The Simplex Method,Integer and Binary Integer Linear Programming,Linear Goal Programming,Network Models - Scope, Definition and Applications,Network Models – Minimal Spanning Tree Algorithms,Network Models - Shortest Path Algorithms,Network Models – Project Management (Pert and CPM),Deterministic Dynamic Programming,Simulation Models. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Will be able to explain the purpose of operations research. | 12, 13, 16, 6, 9 | A, D, E |
| 1.1 Lists the usage areas of operations research | ||
| 1.2 Categorizes the operations research methods | ||
| 1.3 Defines the concept of modeling | ||
| 2. Will be able to explain the logic of optimization. | 12, 13, 16, 6, 9 | A, D, E |
| 2.1 Explains the difference between linear and nonlinear optimization models | ||
| 2.2 Explains the concepts of local optimum and global optimum | ||
| 2.3 Explains constrained and unconstrained optimization concepts | ||
| 3. Will be able to explain linear programming models. | 12, 13, 16, 6, 9 | A, D, E |
| 3.1 Composes linear programming models of simple business problems | ||
| 3.2 Developes integer and binary integer linear programming models | ||
| 3.3 Creates logical constraints using binary variables | ||
| 4. Will be able to solve linear programming models. | 12, 13, 16, 6, 9 | A, D, E |
| 4.1 Explains the logic of graphical solution | ||
| 4.2 Recognizes degenerate cases in linear programming models | ||
| 4.3 Applies the Simplex method | ||
| 4.4 Explains the solution logic of integer linear programming models | ||
| 5. Will be able to design goal programming models. | 12, 13, 16, 6, 9 | A, D, E |
| 5.1 Explains the difference between object and goal | ||
| 5.2 Explains the difference between weighted and priority goal programming | ||
| 5.3 Solves goal programming models using MS Excel Solver | ||
| 6. Will be able to explain the relations among network models and business applications. | 12, 13, 16, 6, 9 | A, D, E |
| 6.1 Designs the network in which the total distance is minimized with minimum spanning tree algorithms | ||
| 6.2 Calculates the shortest path on the network using Shortest Path algorithms | ||
| 6.3 Describes how projects are planned, monitored and controlled using PERT and CPM algorithms | ||
| 7. Will be able to explain the essentials of the simulation. | 12, 13, 16, 6, 9 | A, D, E |
| 7.1 Explains simulation types | ||
| 7.2 Explains Monte Carlo Simulation | ||
| 7.3 Applies simulation in spreadsheets (MS Excel) |
| Teaching Methods: | 12: Problem Solving Method, 13: Case Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Operations Research – Introduction | |
| 2 | Introduction to Optimization | |
| 3 | Linear Programming – Building Models with Linear Programming | |
| 4 | Linear Programming – Solution Concept | |
| 5 | Linear Programming – Sensitivity Analysis | |
| 6 | Linear Programming – The Simplex Method | |
| 7 | Integer and Binary Integer Linear Programming | |
| 8 | Linear Goal Programming | |
| 9 | Network Models - Scope, Definition and Applications | |
| 10 | Network Models – Minimal Spanning Tree Algorithms | |
| 11 | Network Models - Shortest Path Algorithms | |
| 12 | Network Models – Project Management (Pert and CPM) | |
| 13 | Deterministic Dynamic Programming | |
| 14 | Simulation Models |
| Resources |
| [1] Operations Research: An Introduction, Hamdy A. Taha, 11th edition, Pearson, 2023 [2] Introduction to Operations Research, Hillier, F.S. & Lieberman, G.J., 9th Edition, McGrawHill, 2010 |
| [3] Yöneylem Araştırması, Taha, Hamdi A., 6.Basımdan çeviri, Çeviri: Ş.A.Baray ve Ş.Esnaf, Literatür Yayıncılık, 2016 [4] Yöneylem Araştırması: Nicel Karar Teknikleri, Özkan, Şule, 3. Baskı, Nobel Yayıncılık, 2012 [5] Lecture notes will be available at http://mebis.medipol.edu.tr |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında teorik bilgileri tanımlar. | ||||||
| 2 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında gerekli matematiksel ve istatistiki yöntemleri anlatır. | ||||||
| 3 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında gerekli en az bir bilgisayar programını kullanır. | ||||||
| 4 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında gerekli olan mesleki yabancı dil yeterliliğini gösterir. | ||||||
| 5 | Alanına dair projeler hazırlar ve takım çalışmalarını yönetir. | ||||||
| 6 | Mesleki alanda yaşam boyu öğrenmenin bilinciyle uluslararası lojistik ve tedarik zinciri yönetimi alanında gelişmeleri izleyerek kendini sürekli yenileyip edindiği bilgi ve becerileri eleştirel olarak değerlendirir. | ||||||
| 7 | Uluslararası lojistik ve tedarik zinciri yönetimi alanında teorik ve uygulamaya yönelik bilgileri kullanır. | ||||||
| 8 | En az A1 düzeyinde bir yabancı dili kullanarak güncel teknolojileri takip eder, sözlü/yazılı iletişim kurar. | ||||||
| 9 | Örgüt/kurumsal, iş ve toplumsal etik değerlerini benimser ve kullanır. | ||||||
| 10 | Topluma hizmet duyarlılığı çerçevesinde, sosyal sorumluluk ilkelerini benimser ve gerektiğinde inisiyatif alır. | ||||||
| 11 | Disiplinler arası çalışmalar yürütebilmek için farklı disiplinlerde temel bilgileri ve verileri analiz ederek alanında kullanır. | ||||||
| 12 | Uluslararası lojistik ve tedarik zinciri yönetimi sektörlerindeki sorunlar karşısında hem mikro hem makro çerçevede uygun öneriler sunar. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |