IS 803: Advanced Topics in Intelligent Decision Support
AY2006-7, Term 2
Description:
This course explores recent advances in intelligent decision support methodologies. We
will be studying survey and research papers from the following 2 broad topics:
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Centralized Decision Support
o Large-Scale Optimization Models (Advanced Heuristics, Hybrid methods)
o Decision Making under Uncertainty (Robust Optimization)
o Multi-Criteria Decision Support (Evolutionary Algorithms)
o Dynamic/Adaptive Decision Making (Learning Mechanisms: Ants, NN,
Fuzzy)
Decentralized Decision Support
o Distributed Problem Solving, Planning and Scheduling
o Auctions and Agent Negotiations
o Game-theoretic Decision Making
Prerequisites:
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
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IS703 (Decision Support and Optimization)
Discrete Mathematics
Undergraduate-level Probability & Statistics
Time: Thu 9:30-12noon
Location: MR4.6
Instructor: Associate Professor LAU Hoong Chuin
Office Hours: By appointment
References:
Research papers to be distributed in class.
Turban and Aronson. Decision Support Systems and Intelligent Systems (5e). Prentice
Hall, 1998.
G. Weiss (ed). Multi-Agent Systems. A Modern Approach to Distributed Artificial
Intelligence MIT Press, 1999.
E. Burke and G. Kendall (eds) Search Methodologies: Introductory Tutorials in
Optimization and Decision Support Techniques. Springer 2005.
T. G. Crainic and G. Laporte (eds). Fleet Management and Logistics, Kluwer Pub, 1998.
P. Cramton, Y. Shoham, and R. Steinberg (eds), Combinatorial Auctions, MIT Press,
2006
Class Schedule:
Wk
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Topic
Introduction, Large-Scale Decision Models
Large-Scale Decision Models
Student Presentation 1
Distributed Problem Solving
Decision-Making via Auctions and Negotiation
Student Presentation 2
Project Discussion
Recess Break : Project Discussion
Decision-Making under Uncertainty
Multi-Criteria Decision-Support
Dynamic/Adaptive Decision-Support
Student Presentation 3
Game-theoretic Decision Making
Project Presentation
Due Dates
PPT Presentation 1
PPT Presentation 2
Project Proposal
PPT Presentation 3
Final Report
Slides and other materials can be downloaded from
http://www.mysmu.edu/faculty/hclau/is803.html before class.
All deliverables are to be submitted in both hardcopy and email softcopy.
Grading:
Class participation 20%
Student presentation (3 rounds) 30%
Project paper and presentation 50%
Ph.D. students who pick Intelligent Decision Support Systems as depth area need to score
at least an A- in this course. Other Ph.D. students need to score at least a B.
Mode of instructions: Students will learn concepts through active class participation,
research paper reading and presentations, as well as writing a term paper.
Term Paper for IS803
This is an individual project. Each student should complete a research OR survey paper
on decision support or optimization of Supply Chain, Logistics or e-Commerce Systems.
Instructions for research paper (preferred):
 Identify a research problem. The problem must make sense.
 Design a solution to the problem, and show (either analytically or through a
experimental study) that the proposed solution is superior to existing techniques.
 The paper should be no more than 8 double-column pages, in the IEEE format
 The report will be graded on (a) originality and feasibility of the solution, (b)
effectiveness of the solution, and (c) presentation (clarity, organization, English).
Instructions for survey paper:
 Read at least 4 published papers on the selected topic.
 Write a survey paper that covers the following:
o Introduction: motivation, application domain, problem definition
o Summaries of the techniques developed in each paper, clearly highlighting
the strengths and weaknesses of each
o A taxonomy of the various techniques if possible
o Future research directions
 The paper should be no more than 8 double-column pages, in the IEEE format
 The report will be graded on (a) understanding of the chosen papers, (b) critique
of the papers, and (c) presentation (clarity, organization, English).
Project Topic:
The project topic must be approved by the Course Instructor. Otherwise, the report may
be penalized for being out-of-scope.
You can find potential topics from proceedings and journals such as Management Science,
European J. Operational Research, INFORMS J. Computing, Journal of Scheduling,
Journal of Heuristics, Decision Support Systems. (See details on p. 14-15 of BK)
Various papers of interest can be obtained from the Course Instructor.
Project Schedule:
Week 8: Project Proposal
Week 14: Final Report due and Class presentation