| 當期課號 |
1270 |
Course Number |
1270 |
| 授課教師 |
田方治 |
Instructor |
TIEN,FANG CHIH |
| 中文課名 |
決策支援系統 |
Course Name |
Decision Support System |
| 開課單位 |
工業工程與管理系(四日)四A |
Department |
|
| 修習別 |
選修 |
Required/Elective |
Elective |
| 學分數 |
3 |
Credits |
3 |
| 課程目標 |
介紹決策支援系統之基本架構與觀念：包括決策、資料與資料庫管理、模式與模式庫管理，與使用者介面。另對群組決策支援系統（GDSS）與企業資訊系統（EIS）做一簡介。 |
Objectives |
To teach students about the basic concept and the architecture of a DSS, including Decision and Decision Making, Data and Database Management, Models and .Model Base Management, and User Interface. Special topics on GDSS, EIS, and Expert System are also given. |
| 教材 |
Efraim Turban, Jay E. Aronson, “Decision Support Systems and Intelligent Systems,” Prentice Hall International Inc. |
Teaching Materials |
|
| 成績評量方式 |
Lab與case study 50%
課堂參與及出席 10%
Micss Project 20%
Project 20% |
Grading |
Lab and case study 50%
Attendance 10%
Micss Project 20%
Project 20% |
| 教師網頁 |
|
| 教學內容 |
本課程之目的在講授決策支援系統之基本架構及主成要件,並加入其決策分析之現行方法如類神經網路,遺傳演算法等.
Lab及Case Study
本課程將包括3份Case Study及4份Lab，以分組討論於當日課程後進行，其Assignment於指定時間上課前繳，愈時以零分計算。Case Study及Lab 內容如下：
Lab 1: AHP
Lab 2: Neural networks (NeuralShell)
Lab 3: Genetic Algorithms (Evolver or 其他軟體)
Lab 4: Expert System (Exsys)
Case Study I: Roadway Package System
Case Study II: How to invest $10 Million
Case Study II: Personal Computer DIY
課堂參與及出席
課堂參與及出席為主觀之評分，如課堂發表及討論積極者，則卓情加分。
期中及期末考
期中考於本學期之第12週(or第15週)舉行，考試形式另訂之，無期末考。
Project I – (20%)
以Micss 之系統學習於ERP模擬系統作群體決策，以三人一組，於第九週作口頭報告，並撰寫書面報告一份，評分方式為各50%。
Project II - (20%)
依與課人數分組，每組自選DSS相關主題或Case study，於資料蒐集、閱讀，撰寫報告(頁數不限)，並於期末繳交完整報告及參考資料(與磁碟)一份，並於第十二週起做分組簡報。 |
Syllabus |
Decision Support System (DSS) has been an important development in computer area technology. The system is built for helping human making a sincere decision. The objective of this course is to let students realize the basic configuration and components of a DSS. The topics covered in this course include the component of a DSS, data mining, AHP, the basic artificial approaches such as Development of a DSS, intelligent system:(Expert system, Genetic Algorithms, Neural networks, Fuzzy logics) and other rule, case, or data-based driven decision support systems. |