Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366623	Multi Objectives Decision Making	1	3.0	3	★	Hsin-Wei Hsu	GIEM	40	0

Course Plan：

Instructor	Hsin-Wei Hsu　Office Hours
Email	hwhsu@ntut.edu.tw
Last Updated	2026-08-03 13:05:39
Course Objective	決策行為是人們每天在做的活動。然而，除非至少有兩個準則存在，否則不需要做決策，也因此這類問題被稱為多準則決策問題（MCDP）。當替代方案已知時，最終的決定就會被做出，這種類型的問題被稱為多屬性決策問題（MADP）。當替代方案未知，我們必須在某些外部條件下找到它們時，問題被稱為多目標決策問題（MODP）。多準則決策分析（MCDA）是一個通用術語，包括兩類問題的技術，即多屬性決策分析（MADA）和多目標決策分析（MODA），其中目標規劃是它的特殊情況。在這門課程中，我們將介紹這些MCDA方法，從確定性到不確定性。 Making decisions is a daily activity for all of us. However, no decision making occurs unless at least TWO criteria are present, thus this type of problems is called Multi-Criteria Decision Problems (MCDP). When the alternatives are known, the final decision is made and such type of problems is called Multi-Attribute Decision Problems (MADP). When the alternatives are unknown and we have to find them under some external conditions, and the problems are called Multi-Objective Decision Problems (MODP). Multi-Criteria Decision Analysis (MCDA) is a general term that consists of techniques for two types of problems with Multi-Attribute Decision Analysis (MADA) and Multi-Objective Decision Analysis (MODA) in which Goal Programming is its special case. In this course, we will introduce these MCDA methods from certain to uncertainty.
Course Schedule (Week 1-16)	1st week/ Introduction to Multi-criteria Decision Making 2nd week/ Types of Decision Problems and traps 3rd week/ Preference Structure and Decision Support 4th weel/ Architecture Issues and Systematic Decision-making Processes, Decision Goals and Attributes 5th week/ Decision-making Creativity, Brief Introduction to Python code 6th week/ Foundation of Multi-attribute Decision Analysis, Outcome Measurement and Tradeoff 7th week/ Simple Multi-attribute Ranking Technique (SMART), Model of Displaced Ideal 8th week/ (期中考試週) Paper reading and presentation 9th week/ Methods of Outcome Evaluation, Analysis Hierarchy Process (AHP) 10th week/ Data Envelopment Analysis (DEA), Project Assignment Proposal 11th week/ Data Envelopment Analysis (DEA), Fuzzy Evaluation 12th week/ Foundation of Multi-Objective Decision Making 13th week/ Multi-Objective Linear Programming 14th week/ Goal Programming 15th week/ Interactive Programming, Fuzzy Planning 16th week/ Decision tree Bayesian Decision Analysis and the Value of Information
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) ● 參與課程相關作業、作品、實驗、田野調查(Participation in course-related assignments, works, experiments, or field research) ● 遠距教學(Distance learning) Content This course adopts synchronous distance learning. The final group project's online oral presentation and real-time Q&A session will be conducted during the regularly scheduled class time. The meeting screen will be recorded throughout the entire session for future reference. Hours 6 Learning Outcomes The complete presentation or project report must be submitted to the designated location Assessment Percentage Project Assignment Presentation 30% (including presentation & report)
Evaluation and grading policy	Practice of python code and exercises 20% Assignments 15% Paper reading 15% Project proposal 15% Final project 30% attend 5%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Multiple Criteria Decision Analysis for Industrial Engineering: Methodology and Applications, Gerald William Evans, 2017, CRC Press, ISBN 9781498739 825 Multiple Criteria Decision Analysis: An Integrated Approach, Valerie Belton, Theodor J. Stewart, 2001. Springer, ISBN 079237505X Multicriteria Decision Analysis: From Certainty to Uncertainty, Hsiao-Fan Wang, 2004. Tsang Hai, ISBN 9867777557 Introduction to Operations Research, Hiller and Lieberman, 2021. McGraw-Hill
The access to curricular consultation	Instructor: Hsin-Wei Hsu Office: 1134 TEL: +886-2-27712171 ext.2323 hwhsu@cycu.edu.tw Office Hour Tus. 10:00-12:00. Wes. 10:00-13:00 Please make an appointment in advance Lab. mail: sustainable4435@gmail.com
Expanding teaching and resources	● 無 (None)
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG7：可負擔的潔淨能源（Affordable and Clean Energy） SDG13：氣候行動（Climate Action）
Does the course incorporate AI	● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note	SDGs 4 Quality Education