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
Office: E1A-06-10

Teaching Assistants

Ms Zhou Xinxing
Office: Ergo Lab (E1-07-19)
Phone: 6516 6514

Volunteer TAs

Dr. Hu Junfei
Office: SMALab (E1A-05-19)
Phone: 6516 4573

Ms Jiang Yixin
Office: SMALab (E1A-05-19)
Phone: 6516 4573



Zhou Xinxing and Prof. Poh KL

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

For Whom?

Learning Outcomes

The student, upon completion of this module, will be able to:
  1. Use probability trees to model uncertain events and deal with new information.
  2. Apply the rules or axioms of decision theory to decision situations involving uncertainty.
  3. Perform decision analysis using tools such as decision tree, Bayesian networks or influence diagrams, including sensitivity and value of information analysis.
  4. Perform decision analysis under various risk attitudes and risk aversions.
  5. Estimate probabilities from experts or data.
  6. Use the Analytic Hierarchy Process for decision making under multiple criteria.
  7. Develop and analyze complex decision problems and scenarios that require use of a combination of the methods above.
  8. 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)


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

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



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.