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2022　Advanced Operations Research

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Academic unit or major
Undergraduate major in Industrial Engineering and Economics
Instructor(s)
Matsui Tomomi 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Tue3-4(H106)  Fri3-4(H106)  
Group
-
Course number
IEE.A330
Credits
2
Academic year
2022
Offered quarter
1Q
Syllabus updated
2022/4/11
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course covers some mathematical methods in operations research. The topics include, transportation problems, routing problems, network design problems, AHP and DEA. We also review some statistical models including Markov chain, queueing theory, and models for choice behavior. We discuss some topics related to game thery, e.g., stable marriage, spatial interaction model, linear production game, and social choice theory.
The objective of this course is to let students learn basic methods in operations research.

Student learning outcomes

By completing this course, students will have the necessary tools to do the following:
(1) Understand fundamental properties of optimization models
(2) Understand fundamental properties of statistical models
(3) Understand fundamental properties of some models related to game theory

Keywords

optimization problem, statistical model, game theory

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

In each class we give a lecture in the first half and then assign some exercise problems in the last half.

Course schedule/Required learning

  Course schedule Required learning
Class 1 overview of the lecture Details will be given in each lecture.
Class 2 basic algorithms understand the basic algorithms
Class 3 linear programming understand the mathematical structure and algorithms of linear programming
Class 4 duality theorem understand the mathematical structure of duality theorem
Class 5 data envelopment analysis understand the mathematical structure and algorithms of the data envelopment analysis
Class 6 shortest path problem and assignment problem understand the mathematical structure and algorithms of shortest path problems and assignment problems
Class 7 maximum flow problem understand the mathematical structure and algorithms of maximum flow problems
Class 8 graph coloring problem understand the mathematical structure and algorithms of graph coloring problem
Class 9 Mid-term Exam check the level of understanding of the classes 1-８ topics
Class 10 social choice theory understand the mathematical structure of the social choice theory
Class 11 integer programming understand the mathematical structure and algorithms of the integer programming
Class 12 Markov chain understand the mathematical structure of Markov chains
Class 13 analytic hierarchy process understand the mathematical structure and algorithms of analytic hierarchy process
Class 14 queueing theory understand the mathematical structure of queueing theory
Class 15 summary

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

Reference books, course materials, etc.

M. Mori and T. Matsui, ``Operations Research,'' Asakura Publishing Co., Ltd., 2004.

Assessment criteria and methods

To Be Determined (Regular face-to-face lessons: Midterm and final exams 70%, exercise problems 30%.）

Related courses

  • IEE.A206 ： Operations Research
  • IEE.A430 ： Numerical Optimization

Prerequisites (i.e., required knowledge, skills, courses, etc.)

No prerequisites are necessary, but enrollment in related courses is desirable.

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