Course Information and Requirements
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
- 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.
- IE5203 provides partial fullfilment towards the following degrees
as elective module:
- M.Sc (Industrial & Systems
- M.Sc (Supply Chain Management)
(Enterprise Business Analytics)
(Transportation Systems & Management)
(Management of Technology)
(Systems Design & Management)
- M.Sc (Logistics and Supply
Chain Management) (DMP)
- B.Eng (Industrial & Systems
Engineering) (for senior-year students only).
- B.Tech (Industrial & Management
Engineering) (for senior-year students only).
- Other graduate degree programs as free elective as approved.
Graduate students who have prevously taken this module include
those from the Departments of
Statistics and Applied Probability,
Singapore MIT Alliance (CE and AMM&NS),
Electrical & Computer Engineering,
Chemeical & Biomolecular Engineering,
Material Science & Engineering,
Applied Economics, etc.
students who wish to take this module will have the elective DSC4211D Managerial Decision Analysis as anti-requistic.
- 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
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.
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 Examination (open book)
Extra credits of up to 5% may be awarded for good Class and Forum Participations.
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
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,
| ||K.B. Korb and A.E. Nicholson, Bayesian Artificial
Intelligence, Chapman & Hall, 2004.