2021 Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Operations Research
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Shinji Mizuno / Kazuhide Nakata / Akiyoshi Shioura
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Mon (W933) / 5-6 Thu (W933)
- Class
- -
- Course Code
- IEE.A206
- Number of credits
- 200
- Course offered
- 2021
- Offered quarter
- 4Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
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.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | intoroduction to operations research and lienar programming | We instruct in each class |
| Class 2 | linear programming: dual problem and dual theory for linear programming | We instruct in each class |
| Class 3 | the simplex method for linear programming | We instruct in each class |
| Class 4 | the two-phase simplex method for linear programming | We instruct in each class |
| Class 5 | Test level of understanding with exercise problems and summary of thefirst part of the course. -Solve exercise problems covering the contents of classes 1–4 | Test level of understanding and self-evaluate achievement for classes 1–4. |
| Class 6 | nonlinear programming: case of one variable | We instruct in each class |
| Class 7 | nonlinear programming: case of multi variables | We instruct in each class |
| Class 8 | Test level of understanding with exercise problems and summary of thefirst part of the course. -Solve exercise problems covering the contents of classes 6-7 | Test level of understanding and self-evaluate achievement for classes 6-7. |
| Class 9 | network programming: transportation problem | We instruct in each class |
| Class 10 | network programming: shortest path problem | We instruct in each class |
| Class 11 | Test level of understanding with exercise problems and summary of thefirst part of the course. -Solve exercise problems covering the contents of classes 9-10 | Test level of understanding and self-evaluate achievement for classes 9-10. |
| Class 12 | combinatorial optimization: knapsack problem | We instruct in each class |
| Class 13 | combinatorial optimization: branch and bound method | We instruct in each class |
| Class 14 | Test level of understanding with exercise problems and summary of thefirst part of the course. -Solve exercise problems covering the contents of classes 12-13 | Test level of understanding and self-evaluate achievement for classes 12-13. |
Study advice (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 can be found on OCW-i
Evaluation methods and criteria
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
No prerequisites