• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Define Decision Support Systems (DSS) terminologies and explain the complexities of modern DSS

   Contribution to Program Outcomes

   1. Review, interpret and apply the literature on international trade and finance
   2. Work effectively in multi-disciplinary research teams
   3. Communication and Social Competence

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Explain the decision-making process and the requirements for constructing decision support systems

   Contribution to Program Outcomes

   1. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results

   Method of assessment

   1. Written exam
   2. Homework assignment
3. List the models involve in analysis and design of decision support systems

   Contribution to Program Outcomes

   1. Define basic economics, finance and management terminology, theories and concepts
   2. Discover, classify and analyze economic data
   3. Review, interpret and apply the literature on international trade and finance
   4. Being able to evaluate and use the advanced level knowledge on international trade, finance and management that are required in experts and researchers who are much needed by the public and private sectors
   5. COMPETENCIES
   6. Work effectively in multi-disciplinary research teams
   7. Communication and Social Competence
   8. Support his/her ideas with various arguments and present them clearly to a range of audience, formally and informally through a variety of techniques

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Introduction to Decision Making
Week 2:	Introduction to Quantitative Decision Making
Week 3:	Linear Programming (Mathematical Programming)
Week 4:	Microsoft Excell and VBA Programming
Week 5:	Game Theory
Week 6:	Decision Trees
Week 7:	Simulation and its applications
Week 8:	Simulation and its applications Midterm Exam
Week 9:	Forecasting Techniques
Week 10:	Forecasting Techniques
Week 11:	Expert Systems
Week 12:	Artificial Neural Networks
Week 13:	Evolutionary Optimization
Week 14:	Project Presantation
Week 15*:	Project Presentation
Week 16*:	Final Exam
Textbooks and materials:	Vicki L. Sauter, Decision Support Systems for Business Intelligence, 2nd Edition, Wiley
Recommended readings:	Decision Making, Stephen P. Fitzgerald, Capstone Publishing, 2002 Operations Management, Roberta S. Russel, Bernard W. Taylor, Prentice Hall, 1998
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	40
Other in-term studies:		0
Project:		0
Homework:		0
Quiz:		0
Final exam:	16	60
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	4	14
Practice, Recitation:	0	0
Homework:	0	0
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	20	2
Mid-term:	2	1
Personal studies for final exam:	20	2
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)