Course Objectives: The objective of this course is to give students the up-to-date of decision-making concepts, processes, strategies, and technologies that are often used to support decision making in real-world issues coupled with agile approach and industry 4.0 specification.
Students will know how to analyze, to design, to implement and to validate an Intelligent Decision Support System (IDSS). The integration of Artificial Intelligence models and Statistical models, and the knowledge discovery from the data step will be emphasized.
The course consists of foundations and developments of IDSS, software tools for IDSS development, IDSS for Digital Manufacturing Systems, and IDSS applications

Learning Outcomes: The students on the completion of this course would be able to

CLO1 – Explain concepts of a decision support system in terms of an interactive system providing information, tools and models and its effects on Industry 4.0. (Understanding)
CLO2 – Apply techniques of IDSS (e.g. artificial neural networks, machine learning, rule-based systems, etc.) and validate IDSS techniques to solve a complex industrial problem. (Applying, Valuing)
CLO3 – Identify decision factors, models, and analysis of intelligent decision support systems (IDSS) to support a smart production. (Analyzing, Receiving)
CLO4 – Appraise the frameworks of IDSS. (Evaluating)
CLO5 – Design a knowledge-based system for smart production. (Creating, Teamwork and Communication Skills)

Prerequisite: None

Course Outline:

Week	Topic	Workshop	Learning Materials	Teaching Materials	Note
I. IDSS Foundation and Development
1	The Needs of Decision Support Tools	MSIE-08-T-M1S1-W01	MSIE-08-L-M1S1	MSIE-08-T-M1S1
2	Modelling of Decision Process	MSIE-08-T-M1S2-W01	MSIE-08-L-M1S2	MSIE-08-T-M1S2
3	IDSS Architecture, Analysis, Design, Requirements, and Validation	MSIE-08-T-M1S3-W01	MSIE-08-L-M1S3	MSIE-08-T-M1S3
4	Impact of IDSS in Industrial Performance Economic Impact of IDSS in industry	MSIE-08-T-M1S4-W01	MSIE-08-L-M1S4	MSIE-08-T-M1S4
5	Agile Approach for Smart Production	MSIE-08-T-M1S5-W01	MSIE-08-L-M1S5	MSIE-08-T-M1S5
II. Software Tools for IDSS Development
6	The Analytic Hierarchy Process (AHP)	IDSS Development by AHP	MSIE-08-L-M2S1	MSIE-08-T-M2S1-4
7	R-software	Multi-objective Decision Analysis in R	MSIE-08-L-M2S2		Multi Objective Decision Analysis in R
8	RapidMiner	RapidMiner & Data Mining	MSIE-08-L-M2S3		Data Mining for the Masses
9	WEKA	Simple Classifiers with WEKA	MSIE-08-L-M2S4		Data Mining with WEKA
10	Deep Learning for Smart Production	MSIE-08-T-M2S5	MSIE-08-L-M2S5	MSIE-08-T-M2S5
III. IDSS for Digital Manufacturing Systems
11	– Artificial Intelligence and DSS – Knowledge Acquisition and Representation	MSIE-08-T-M3S1-W01	MSIE-08-L-M3S1	MSIE-08-T-M3S1
12	Predictive Models	MSIE-08-T-M3S2-W01	MSIE-08-L-M3S2	MSIE-08-T-M3S2
13	Uncertainty Models	MSIE-08-T-M3S3-W01	MSIE-08-L-M3S3	MSIE-08-T-M3S3
14	Industrial Applications	MSIE-08-T-M3S4-W01	MSIE-08-L-M3S4	MSIE-08-T-M3S4
15	Knowledge-based Systems for Smart Production	MSIE-08-T-M3S5-W01	MSIE-08-L-M3S5	MSIE-08-T-M3S5

Flashback:

Laboratory Session: None

Textbooks: No designated textbook, but class notes and handouts will be provided.

Reference Books:

1. Gupta, J.N.D., Forgionne, G.A., and Manuel, M.T., Intelligent Decision-making Support Systems: Foundations, Applications and Challenges, Springer, 2006
2. Iantovics, B., and Kountchev, R., Advanced Intelligent Computational Technologies and Decision Support Systems, Springer, 2014
3. Kumer. K., Zindani, D. and Davim, J.P., Digital Manufacturing and Assembly Systems in Industry 4.0, CRC Press, 2019
4. Tweedale, J.W., Neves-Silva, R., Jain, L.C., Phillips-Wren, G., Watada, J., and Howlett, R.J., Intelligent Decision Technology Support in Practice, Springer, 2016
5. Valencia-Garcia, R, Paredes-Valverde, M.A., Salas-Zarate, M.P. and Alor-Hernandez, Giner., Exploring Intelligent Decision Support Systems, Springer, 2018

Journals and Magazines:

1. Decision Support Systems, Elsevier
2. Journal of Decision Systems, Taylor & Francis LTD
3. International Journal of Decision Support System Technology, IGI Global
4. International Journal of Intelligent Systems, Wiley-Blackwell
5. IEEE Intelligent Systems, IEEE
6. Expert Systems, Wiley

Teaching and Learning Methods:

The Collaboration is the main idea of teaching. Students are actively participated in the class by talking with each other and listening to other opinions. The learning methods include case study, group discussion, individual assignment, practical exercises, simulation, field class, and a group project.

Time Distribution and Study Load:

Lectures: 15 hours
Workshop: 60 hours
Self-study/Group Project: 75 hours

Evaluation Scheme:

The final grade will be computed according to the following weight distribution: Case Studies 20%, Practical Exercises 10%, Assignments 10%, Portfolio 5%, Peer Assessment 5%, Oral Presentation 10%, Project 20%, and Open Exam 20%. In final grading,

An “A” would be awarded if a student shows a deep understanding of the knowledge learned through assignments, project works, and exam results.
A “B” would be awarded if a student shows an overall understanding of all topics.
A “C” would be given if a student meets below average expectation in understanding and application of basic knowledge.
A “D” would be given if a student does not meet expectations in both understanding and application of the given knowledge.

Developer: Suriya Jirasatitsin (suriya.j@psu.ac.th, PSU), Warapoj Meethom (warapoj.m@eng.kmutnb.ac.th, KMUTNB) and Thitipong Jamrus (thitja@kku.ac.th, KKU)