Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| SCHEDULING | - | 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 | Assoc.Prof. Yasin GÖÇGÜN |
| Name of Lecturer(s) | Prof.Dr. Hakan TOZAN |
| Assistant(s) | |
| Aim | The students who succeeded the course will be able to identify, formulate and solve various problems in the area of scheduling (deterministic and stochastic). |
| Course Content | This course contains; Introduction ,Deterministic Models: Preliminaries: Framework and Notation,Deterministic Models: Preliminaries: Classes of Schedules and Complexity Hierarchy,Deterministic Single Machine Models (Total weighted completion time, maximum lateness, number of tardy jobs),Deterministic Single Machine Models (Total weighted tardiness, makespan),Deterministic Parallel Machine Models-1,Deterministic Parallel Machine Models-2,Deterministic Flowshops (with limited/unlimited intermadiate storage),Felexible Flow Shops,Open Shop Scheduling,Jop Shop Scheduling,General Purpose Procedures for Deterministic Schedule,Stochastic Moldels: Preliminaries,Stochastic Models. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Students will be able to model deterministic single machine problems | 10, 16, 6, 9 | A, E |
| Students will be able to model deterministic parallel machine problems | 10, 16, 6, 9 | A, E |
| Students will be able to model open shop scheduling problems | 10, 16, 6, 9 | A, E |
| Students will be able to model job shop scheduling problems | 10, 16, 6, 9 | A, E |
| Teaching Methods: | 10: Discussion Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction | |
| 2 | Deterministic Models: Preliminaries: Framework and Notation | |
| 3 | Deterministic Models: Preliminaries: Classes of Schedules and Complexity Hierarchy | |
| 4 | Deterministic Single Machine Models (Total weighted completion time, maximum lateness, number of tardy jobs) | |
| 5 | Deterministic Single Machine Models (Total weighted tardiness, makespan) | |
| 6 | Deterministic Parallel Machine Models-1 | |
| 7 | Deterministic Parallel Machine Models-2 | |
| 8 | Deterministic Flowshops (with limited/unlimited intermadiate storage) | |
| 8 | Felexible Flow Shops | |
| 10 | Open Shop Scheduling | |
| 11 | Jop Shop Scheduling | |
| 12 | General Purpose Procedures for Deterministic Schedule | |
| 13 | Stochastic Moldels: Preliminaries | |
| 14 | Stochastic Models |
| Resources |
| Michael Pinedo, Scheduling: Theory, Algorithms, and Systems, 4th Edition, Springer. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | |||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | X | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | X | |||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| 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 | 4 | 15 | 60 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 30 | 30 | |||
| General Exam | 1 | 40 | 40 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 172 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(172/30) | 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 |
|---|---|---|---|---|---|
| SCHEDULING | - | 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 | Assoc.Prof. Yasin GÖÇGÜN |
| Name of Lecturer(s) | Prof.Dr. Hakan TOZAN |
| Assistant(s) | |
| Aim | The students who succeeded the course will be able to identify, formulate and solve various problems in the area of scheduling (deterministic and stochastic). |
| Course Content | This course contains; Introduction ,Deterministic Models: Preliminaries: Framework and Notation,Deterministic Models: Preliminaries: Classes of Schedules and Complexity Hierarchy,Deterministic Single Machine Models (Total weighted completion time, maximum lateness, number of tardy jobs),Deterministic Single Machine Models (Total weighted tardiness, makespan),Deterministic Parallel Machine Models-1,Deterministic Parallel Machine Models-2,Deterministic Flowshops (with limited/unlimited intermadiate storage),Felexible Flow Shops,Open Shop Scheduling,Jop Shop Scheduling,General Purpose Procedures for Deterministic Schedule,Stochastic Moldels: Preliminaries,Stochastic Models. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Students will be able to model deterministic single machine problems | 10, 16, 6, 9 | A, E |
| Students will be able to model deterministic parallel machine problems | 10, 16, 6, 9 | A, E |
| Students will be able to model open shop scheduling problems | 10, 16, 6, 9 | A, E |
| Students will be able to model job shop scheduling problems | 10, 16, 6, 9 | A, E |
| Teaching Methods: | 10: Discussion Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction | |
| 2 | Deterministic Models: Preliminaries: Framework and Notation | |
| 3 | Deterministic Models: Preliminaries: Classes of Schedules and Complexity Hierarchy | |
| 4 | Deterministic Single Machine Models (Total weighted completion time, maximum lateness, number of tardy jobs) | |
| 5 | Deterministic Single Machine Models (Total weighted tardiness, makespan) | |
| 6 | Deterministic Parallel Machine Models-1 | |
| 7 | Deterministic Parallel Machine Models-2 | |
| 8 | Deterministic Flowshops (with limited/unlimited intermadiate storage) | |
| 8 | Felexible Flow Shops | |
| 10 | Open Shop Scheduling | |
| 11 | Jop Shop Scheduling | |
| 12 | General Purpose Procedures for Deterministic Schedule | |
| 13 | Stochastic Moldels: Preliminaries | |
| 14 | Stochastic Models |
| Resources |
| Michael Pinedo, Scheduling: Theory, Algorithms, and Systems, 4th Edition, Springer. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | X | |||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | X | |||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | X | |||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |