About IE5203

Principal Instructor

Dr. POH, Kim Leng

Associate Professor

Department of Industrial Systems Engineering & Management

National University of Singapore.

email: pohkimleng@nus.edu.sg | website | facebook

office: E1A-06-10 | phone: 6516 2193

Teaching Assistant

Zoe Nguyen

email: minh.t.nguyen@u.nus.edu

Lecture Schedule

Lecture Group 1: Tuesday 2 pm to 5 pm, LT4

Lecture Group 2: Tuesday 6 pm to 9 pm, LT1

Course Descriptions

IE5203 (4 units). This course teaches the necessary analytical knowledge and practical skills for improving decision-making processes in engineering, management 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, influence diagrams, risk preference to multi-attribute utility theory and analytic hierarchy process are covered.

Course Outline

  1. Introduction to Decision Analysis
  2. Modeling Uncertainty
  3. Decision Theory
  4. Basic Decision Analysis: Decision Tree, Sensitivity Analysis, Value of Information, Stochastic Dominance
  5. Decision Modeling and Analysis using Bayesian Networks and Influence Diagrams
  6. Advanced Decision Analysis: Risk Preferences
  7. Probability Distribution Assessment
  8. Complex Decision Problems: The Decision Analysis Process/Cycle
  9. MCDM I: Analytic Hierarchy Process (AHP)
  10. MCDM II: Multi-Attribute Utility Theory and Trade-off Analysis
  11. Options Thinking and Valuation in Decision Analysis
  12. Software: DPL, Netica, YAAHP, Excel, Python

Learning Outcomes

Upon completion of this course you 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 different risk attitudes and degree of risk aversions.
  5. Assess probability distributions from experts and/or data.
  6. Use the Analytic Hierarchy Process (AHP) 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 integrated analysis using selected professional decision analysis software.

Assessments

  • Assignments: 5%
  • Mid Term: 20%
  • Term Paper: 25%
  • Final Exam: 50%

Application Term Paper

Students will have the opportunity to work on a term paper on the application of decision analysis to a realistic real-world problem either from their professional work or personal life using techniques and software learned in this course. (more info)

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 class website.

Required Readings

References

  1. R.T. Clemen and T. Reilly, Making hard decisions with DecisionTools, 3rd Edition, Duxbury Thomson Learning, 2014.
  2. 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.
  3. T.L. Saaty, The Analytic Hierarchy Process, McGraw Hill, New York, 1980 (RWS edition 1990).
  4. R.L. Keeney and H. Raiffa, Decisions with Multiple Objectives: Preferences and Value Tradeoffs, 1975.