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- Common courses
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- 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
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- 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)
- Mizuno Shinji Nakata Kazuhide Kitahara Tomonari
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon5-6(W934) Thr5-6(W934)
- Group
- -
- Course number
- IEE.A206
- Credits
- 2
- Academic year
- 2016
- Offered quarter
- 4Q
- Syllabus updated
- 2016/4/27
- 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, Quadratic 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 programming, Combinatorial optimization
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Attendance is taken in every class.
Students are required to read the text before coming to class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | intoroduction to operations research and lienar programming | |
| Class 2 | linear programming: dual problem and dual theory for linear programming | |
| Class 3 | Test level of understanding with exercise problems and summary of thefirst part of the course. -Solve exercise problems covering the contents of classes 1–2 | Test level of understanding and self-evaluate achievement for classes 1–2. |
| Class 4 | the simplex method for linear programming | |
| Class 5 | the two-phase simplex method for linear programming | |
| Class 6 | Test level of understanding with exercise problems and summary of thefirst part of the course. -Solve exercise problems covering the contents of classes 4-5 | Test level of understanding and self-evaluate achievement for classes 4–5. |
| Class 7 | nonlinear programming: modeling and optimal condition | |
| Class 8 | nonlinear programming: case of one variable | |
| Class 9 | nonlinear programming: case of multi variables | |
| Class 10 | Test level of understanding with exercise problems and summary of thefirst part of the course. -Solve exercise problems covering the contents of classes 8–9 | Test level of understanding and self-evaluate achievement for classes 8-9. |
| Class 11 | network programming: transportation problem | |
| Class 12 | network programming: shortest path problem | |
| Class 13 | Test level of understanding with exercise problems and summary of thefirst part of the course. -Solve exercise problems covering the contents of classes 11–12 | Test level of understanding and self-evaluate achievement for classes 11-12. |
| Class 14 | combinatorial optimization: knapsack problem | |
| Class 15 | combinatorial optimization: branch and bound method |
Textbook(s)
None required
Reference books, course materials, etc.
Course materials can be found on OCW-i
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 (70%) and exercise problems (30%).
Related courses
- IEE.A330 ： Advanced Operations Research
- IEE.A331 ： OR and Modeling
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisites
