KA240401 Decision-Making

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA240401 Subject Classification Specialized Education

Subject Name 意思決定

Subject Name
（Katakana）イシケッテイ

Subject Name in
English Decision-Making

Instructor ZHENG JUNJUN

Instructor
(Katakana) テイ　シュンシュン

Campus Higashi-Hiroshima Semester/Term 3rd-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Fri1-4：ENG 220

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Lecture-based

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords Linear programming, integer programming, constraint satisfaction problems, network optimization, dynamic programming, queuing theory, simulation, decision-making models

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) This course is positioned as a foundational specialized subject within the Information Science program. It systematically covers engineering methods such as linear and integer programming, network optimization, dynamic programming, and simulation, developing students' logical decision-making skills and problem-solving abilities essential for advanced specialized courses and graduation research.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic.

Data Science Program
（Abilities and Skills）
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
・D2. Ability to take charge of organizational strategy and planning based on statistical evidence by making full use of a wide range of knowledge and techniques in data science.
（Comprehensive Abilities）
・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability.

Intelligence Science Program
（Abilities and Skills）
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.

Class Objectives
/Class Outline The goal of this course is to systematically introduce theories and methods for mathematically analyzing and solving decision-making problems from an engineering perspective, enabling students to apply these techniques practically. Students will learn fundamental methods including linear programming, integer programming, constraint satisfaction problems, network optimization, dynamic programming, queuing theory, and simulation. The course aims to develop the ability to apply these methods effectively to real-world problems in fields such as logistics, AI, manufacturing, transportation, finance, and healthcare.

Class Schedule lesson1 Review of linear algebra
(Matrices, vectors, simultaneous equations, Gaussian elimination)
lesson2 Fundamentals of linear programming
(Modeling, standard form, graphical solutions with two variables)
lesson3 Simplex method
(Basics, pivot operations, optimality conditions, sensitivity analysis)
lesson4 Duality theory and applications
(Dual theorem, shadow prices, economic interpretation, introduction to transportation problems)
lesson5 Transportation and assignment problems
(MODI method, Hungarian algorithm, specialized linear programming applications)
lesson6 Fundamentals of integer programming
(Branch-and-bound method, formulation strategies, simple facility location examples)
lesson7 Introduction to constraint satisfaction problems (CSP)
(Constraint networks, propagation techniques, backtracking search)
lesson8 Comparison and applications of CSP and integer programming
(Scheduling, Sudoku, modeling strength)
lesson9 Network optimization (1)
(Shortest paths: Dijkstra, Bellman-Ford algorithms, graph formulations)
lesson10 Network optimization (2)
(Maximum flow: Ford-Fulkerson, minimum cost flow, network simplex method)
lesson11 Dynamic programming (DP)
(States, recursive relationships, examples like knapsack and routing problems)
lesson12 Basics of queuing theory
(M/M/1 model, Poisson processes, average waiting time and system evaluation)
lesson13 Introduction to simulation
(Discrete-event simulation, Monte Carlo method and application fields)
lesson14 Case studies
(Logistics, AI, real-world OR applications in manufacturing, transportation, finance, healthcare)
lesson15 Review and comprehensive exercise
(Overall review, comprehensive problems, discussion)

Midterm test and final examination will be conducted.

Text/Reference
Books,etc. Reference books:
・「意思決定の数理」，西崎一郎著，森北出版株式会社（2017）
・「意思決定のための数理モデル入門」，今野浩，後藤順哉共著，朝倉書店（2011）
・「Python意思決定の数理入門」，橋本洋志，牧野浩二，佐々木智典共著，Ohmsha（2022）

PC or AV used in
Class,etc. Handouts, moodle

(More Details)

Learning techniques to be incorporated Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Fieldwork / Survey, Post-class Report

Suggestions on
Preparation and
Review Before each class, students are encouraged to preview essential terms and basic concepts using distributed materials and recommended references. For mathematical techniques such as linear and integer programming taught in the first half of the course, it is particularly effective to become familiar with foundational calculation methods in advance. After classes, students should solve exercises applying methods learned during lectures to reinforce theoretical understanding and calculation accuracy. For later topics like network optimization, dynamic programming, queuing theory, and simulation, reviewing practical application examples of these theories is strongly recommended.

Others:
Students are encouraged to participate in the course evaluation surveys conducted for improving class quality. Feedback will be provided by the instructor to support continuous improvement.

Requirements

Grading Method Grades will be determined based on a comprehensive evaluation of regular performance (attendance, class attitude, and contribution to discussions), assignment reports, midterm test, and final examination.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA240401	Subject Classification	Specialized Education
Subject Name	意思決定
Subject Name （Katakana）	イシケッテイ
Subject Name in English	Decision-Making
Instructor	ZHENG JUNJUN
Instructor (Katakana)	テイ　シュンシュン
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Fri1-4：ENG 220
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face
Lecture-based
Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	Linear programming, integer programming, constraint satisfaction problems, network optimization, dynamic programming, queuing theory, simulation, decision-making models
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	This course is positioned as a foundational specialized subject within the Information Science program. It systematically covers engineering methods such as linear and integer programming, network optimization, dynamic programming, and simulation, developing students' logical decision-making skills and problem-solving abilities essential for advanced specialized courses and graduation research.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic. Data Science Program （Abilities and Skills）・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. ・D2. Ability to take charge of organizational strategy and planning based on statistical evidence by making full use of a wide range of knowledge and techniques in data science. （Comprehensive Abilities）・D3. Ability to overlook social needs and issues that are intertwined in a complex manner and to solve issues with quantitative and logical thinking based on data, a multifaceted perspective, and advanced information analysis ability. Intelligence Science Program （Abilities and Skills）・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. ・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
Class Objectives /Class Outline	The goal of this course is to systematically introduce theories and methods for mathematically analyzing and solving decision-making problems from an engineering perspective, enabling students to apply these techniques practically. Students will learn fundamental methods including linear programming, integer programming, constraint satisfaction problems, network optimization, dynamic programming, queuing theory, and simulation. The course aims to develop the ability to apply these methods effectively to real-world problems in fields such as logistics, AI, manufacturing, transportation, finance, and healthcare.
Class Schedule	lesson1 Review of linear algebra (Matrices, vectors, simultaneous equations, Gaussian elimination) lesson2 Fundamentals of linear programming (Modeling, standard form, graphical solutions with two variables) lesson3 Simplex method (Basics, pivot operations, optimality conditions, sensitivity analysis) lesson4 Duality theory and applications (Dual theorem, shadow prices, economic interpretation, introduction to transportation problems) lesson5 Transportation and assignment problems (MODI method, Hungarian algorithm, specialized linear programming applications) lesson6 Fundamentals of integer programming (Branch-and-bound method, formulation strategies, simple facility location examples) lesson7 Introduction to constraint satisfaction problems (CSP) (Constraint networks, propagation techniques, backtracking search) lesson8 Comparison and applications of CSP and integer programming (Scheduling, Sudoku, modeling strength) lesson9 Network optimization (1) (Shortest paths: Dijkstra, Bellman-Ford algorithms, graph formulations) lesson10 Network optimization (2) (Maximum flow: Ford-Fulkerson, minimum cost flow, network simplex method) lesson11 Dynamic programming (DP) (States, recursive relationships, examples like knapsack and routing problems) lesson12 Basics of queuing theory (M/M/1 model, Poisson processes, average waiting time and system evaluation) lesson13 Introduction to simulation (Discrete-event simulation, Monte Carlo method and application fields) lesson14 Case studies (Logistics, AI, real-world OR applications in manufacturing, transportation, finance, healthcare) lesson15 Review and comprehensive exercise (Overall review, comprehensive problems, discussion) Midterm test and final examination will be conducted.
Text/Reference Books,etc.	Reference books: ・「意思決定の数理」，西崎一郎著，森北出版株式会社（2017）・「意思決定のための数理モデル入門」，今野浩，後藤順哉共著，朝倉書店（2011）・「Python意思決定の数理入門」，橋本洋志，牧野浩二，佐々木智典共著，Ohmsha（2022）
PC or AV used in Class,etc.	Handouts, moodle
(More Details)
Learning techniques to be incorporated	Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Fieldwork / Survey, Post-class Report
Suggestions on Preparation and Review	Before each class, students are encouraged to preview essential terms and basic concepts using distributed materials and recommended references. For mathematical techniques such as linear and integer programming taught in the first half of the course, it is particularly effective to become familiar with foundational calculation methods in advance. After classes, students should solve exercises applying methods learned during lectures to reinforce theoretical understanding and calculation accuracy. For later topics like network optimization, dynamic programming, queuing theory, and simulation, reviewing practical application examples of these theories is strongly recommended. Others: Students are encouraged to participate in the course evaluation surveys conducted for improving class quality. Feedback will be provided by the instructor to support continuous improvement.
Requirements
Grading Method	Grades will be determined based on a comprehensive evaluation of regular performance (attendance, class attitude, and contribution to discussions), assignment reports, midterm test, and final examination.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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