2021　Operations Research

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Undergraduate major in Industrial Engineering and Economics
Instructor(s)
Mizuno Shinji  Nakata Kazuhide  Shioura Akiyoshi
Class Format
Lecture
Media-enhanced courses
Day/Period(Room No.)
Mon5-6(W933)  Thr5-6(W933)
Group
-
Course number
IEE.A206
Credits
2
2021
Offered quarter
4Q
Syllabus updated
2021/4/6
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 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.

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.

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

No prerequisites