▶Japanese
Post to SNS
- Home
- > School of Engineering
- > Undergraduate major in Industrial Engineering and Economics
- > Operations Research
- School of Science
-
School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Shioura Akiyoshi
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Mon5-6(W641) Thr3-4(W641)
- Group
- -
- Course number
- IEE.A206
- Credits
- 2
- Academic year
- 2022
- Offered quarter
- 4Q
- Syllabus updated
- 2022/10/4
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course studies properties and solution methods for fundamental optimization models, which include Linear Programming, Nonlinear Programming, Network Programming and Combinatorial Optimization Problems.
The technology of operations research is useful to do decision making for various problems in management sciences. Knowledge and ability acquired through this course will help students to solve real optimization problems in the future.
Student learning outcomes
By the end of this course, students will be able to:
・Understand fundamental properties of linear programming and use the simplex method.
・Understand fundamental properties of nonlinear programming and use the steepest descent method and the Newton method.
・Understand fundamental properties of network programming problems and use its solution methods.
・Understand fundamental properties of Knapsack problems and use the branch and bound method.
Keywords
Linear programming, Nonlinear programming, network optimization, Combinatorial optimization
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
In each lecture, lecture materials will be uploaded to T2SCHOLA so that students can read and understand the content.
Lectures will be given in person.
Before the end of the class, exercises related to the contents of the day's class will be presented, and students are expected to solve them before the next class as a report.
Submission of the report is optional, but points will be added to the final grade depending on how well the report is completed.
A mid-term exam and a final exam will be given to check the level of understanding.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | intoroduction to operations research and lienar programming | Instructions will be given in each class. |
| Class 2 | linear programming | Instructions will be given in each class. |
| Class 3 | linear programming | Instructions will be given in each class. |
| Class 4 | linear programming | Instructions will be given in each class. |
| Class 5 | linear programming | Instructions will be given in each class. |
| Class 6 | combinatorial optimization | Instructions will be given in each class. |
| Class 7 | mid-term exam | Instructions will be given in each class. |
| Class 8 | network optimization | Instructions will be given in each class. |
| Class 9 | network optimization | Instructions will be given in each class. |
| Class 10 | network optimization | Instructions will be given in each class. |
| Class 11 | nonlinear optimization | Instructions will be given in each class. |
| Class 12 | nonlinear optimization | Instructions will be given in each class. |
| Class 13 | nonlinear optimization | Instructions will be given in each class. |
| Class 14 | final exam | Instructions will be given in each class. |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None required
Reference books, course materials, etc.
Course materials will be uploaded on T2SCHOLA.
Assessment criteria and methods
Students will be assessed on their understanding of linear programming, nonlinear programming, network programming, and combinatorial optimization, and their ability to apply them to solve problems.
Students' course scores are based on midterm and final exams (80%) and reports (20%).
Related courses
- IEE.A330 ： Advanced Operations Research
- IEE.A331 ： OR and Modeling
- IEE.A202 ： Mathematics for Industrial Engineering and Economics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisites
