Course Information and Requirements

Course Professor

Associate Professor Poh Kim Leng, Ph.D.
Department of Industrial & Systems Engineering
National University of Singapore

Phone: 6516 2193
Email: isepohkl@nus.edu.sg
Office: E1A-06-10

Teaching Assistants

Nil for summer

Course Descriptions

IE5203 (4 MC): This module teaches the necessary analytical knowledge and practical skills for improving decision-making processes in engineering and business environments. This is achieved by providing a paradigm based on normative decision theory and a set of prescriptive tools and computational techniques using state-of-the art software with which a stake holder can systematically analyze a complex and uncertain decision situation leading to clarity of action. Topics from utility theory and influence diagrams modeling to multi-attribute utility theory and analytic hierarchy process are covered.

Course Outline

Introduction
Uncertainty
Decision Theory
Basic Decision Analysis
Sensivity Analysis
Value of Information Analysis
Stochastic Dominance Analysis
Bayesian Networks
Influence Diagrams
Risk and Time Preferences
Assessment of Probabilities
The Decision Analysis Cycle
Analytic Hierarchy Process
Multi-Attribute Decision Analysis
Software: Excel, Treeplan, Sensit, Netica, DPL and Expert Choice.

For Whom?

IE5203 provides partial fullfilment towards the following degrees as elective module:
1. M.Sc. (Industrial & Systems Engineering)
2. M.Sc. (Transportation Systems & Management)
3. M.Sc. (Management of Technology)
4. M.Sc. (Systems Design & Management)
5. M.Sc. (Logistics and Supply Chain Management) (DMP/CDMP)
6. B.Eng. (Industrial & Systems Engineering) (for senior-year students only).
7. B.Tech. (Industrial & Management Engineering) (for senior-year students only).
8. Other graduate degree programs as free elective as approved. Graduate students who have prevously taken this module include those from the Departments of Computer Science, Information Systems, Singapore MIT Alliance (CE and AMM&NS), Electrical & Computer Engineering, Civil Engineering, Chemeical & Biomolecular Engineering, Material Science & Engineering, Mechanical Engineering, Building Science, Applied Physics, Applied Economics, etc.
MSc(SCM) students who wish to take this module will have the elective DSC4211D Managerial Decision Analysis as anti-requistic.
IE5203 may be taken by University Scholars as a ride-on for their Independent Study Module. They will do a special term paper instead of the regular term paper for this module.
IE5203 may be taken by professionals from the industry with relevant background in basic probabilities and statistics to enhance their analytical decision making skills or for personal enrichment. They may enrol as special non-examination students through Office of Professional Engineering & Executive Education, OPE3

Learning Outcomes

The student, upon completion of this module, will be able to:

Use probability trees to model uncertain events and deal with new information.
Apply the rules or axioms of decision theory to decision situations involving uncertainty.
Perform decision analysis using tools such as decision tree, Bayesian networks or influence diagrams, including sensitivity and value of information analysis.
Perform decision analysis under various risk attitudes and risk aversions.
Estimate probabilities from experts or data.
Use the Analytic Hierarchy Process for decision making under multiple criteria.
Develop and analyze complex decision problems and scenarios that require use of a combination of the methods above.
Perform decision analysis using computer software such as Excel, Netica, DPL, or Expert Choice.

Course Requirements

Term Paper

An application term paper is required for IE5203. Students will work individually on a reasonably realistic decision problem either from their professional work or personal life using the decision analysis techniques and software learned in this course. (More about the application term paper)

Homeworks

Homework exercises are given at the end of each chapter in the lecture notes. They will not be graded. You are required to attempt them as they will help you understand the class materials better. Solutions to selected questions will be discussed in class. All solutions will be posted on the website.

Final Grading

	Weight
Assignments	5%
Mid-Term	20%
Term Paper	25%
Final Examination (open book)	50%

Extra credits of up to 5% may be awarded for good Class and Forum Participations.

Reading Materials

Required

K.L. Poh, IE5203 Lecture Notes, 2013 Edition.

Selected References (see lecture notes for more)

	R.T. Clemen and T. Reilly, Making hard decisions with DecisionTools. Duxbury Thomson Learning, 2001.
	R.A. Howard and J.E. Matheson (Editors), Readings on the Principles and Applications of Decision Analysis, Strategic Decision Group, Vol. I and II, 1983. (The table of contents and the full text of some articles from this two-volume reader are available here, hosted by the Stanford Decisions and Ethics Center).
	T.L. Saaty, The Analytic Hierarchy Process, McGraw Hill, New York, 1980 (RWS edition 1990).
	T.L. Saaty, Decision Making for Leaders: The Analytic Hierarchy Process for Decision in a Complex World. Revised edition. RWS publications, 1995.
	C.W. Kirkwood, Strategic Decision Making: Multiobjective Decsion Analysis with Spreadsheets, Duxbury, 1997.
	K.B. Korb and A.E. Nicholson, Bayesian Artificial Intelligence, Chapman & Hall, 2004.