About TIE4203

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

Graduate Tutor

Zoe Nguyen

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

Course Descriptions

TIE4203 (4 units). This course introduces the fundamental principles and practices for decision modelling and risk analysis in industrial engineering and operations management. It presents a set of analytical methods and tools with which stakeholders can deal with complex and uncertain decision situations leading to clear and defensible actions.

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 and Lab: 10%
  • Mid Term: 20%
  • Project: 25%
  • Final Exam: 45%

Project on Application of DA to Real World Problems

The project aims to provide opportunity for students to apply the analytical skills and software tools learnt in this module to a selected real-world problem to help decision makers and stakeholders make clear and defensible decisions. Students are expected to apply the Decision Analysis Cycle or Process (Chapter 8) to the problem using appropriate software. The AHP (Chapter 9) MAUT methods (Chapter 10) may also be used to perform integrated decision modeling and analysis.

Tutorials

There will be weekly tutorial sessions from Week 3. Tutorials 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 by the tutor. All solutions will be posted on the website by the tutor 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.