IS703: Decision Support and Optimization
AY2006-7, Term 1
Description:
This course provides methodological foundations of intelligent decision support systems.
It covers:
a.
Classical algorithmic paradigms - Divide/Conquer Greedy, Dynamic Programming
b. Graphs and Networks Models and Algorithms
c.
Mathematical and Constraint Modeling
d. Local Search and Advanced Heuristics
Throughout the course, we will present a wide range of problems in supply chains, from
strategic issues such as network design, to tactical issues such as procurement & sourcing,
to operational issues such as real-time supply chain optimization. The course aims to
equip students for decision support and optimization research, particularly in supply
chain systems design and analysis.
Prerequisites:


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:
Main Texts:
(BK) Edmund Burke and Graham Kendall (eds) Search Methodologies: Introductory
Tutorials in Optimization and Decision Support Techniques. Springer 2005.
(CLRS) Cormen, Leiserson, Rivest and Stein. Introduction to Algorithms (2nd ed), MIT
Press, 2001.
Supplementary References:
D. Simchi-Levi, X. Chen and J. Bramel. The Logic of Logistics: Theory, Algorithms, and
Applications for Logistics and SCM (2nd ed), Springer, 2005.
Hartmut Stadtler and Christoph Kilger (eds). Supply Chain Management And Advanced
Planning: Concepts, Models, Software and Case Studies (3rd ed). Springer, 2004.
Carlos Daganzo. Logistics Systems Analysis (3rd ed), Springer-Verlag, 1999.
Class Schedule:
Wk
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Topic
Introduction and Computational Complexity
Classics I (Divide and Conquer, Sorting and Selection)
Classics II (Greedy and Dynamic Programming)
Classics III (Graphs and Networks)
Optimization Models I (Mathematical Programming)
Optimization Models II (Constraint Programming)
Optimization Models III (Local Search)
Recess Break (Mid-Term)
Advanced Heuristics I (Meta-heuristics)
Advanced Heuristics II (Evolutionary Computing)
Supply Chains I (Concepts)
Supply Chains II (Applications)
Project Presentation
No Final Exams!
Event
Due: Assignment 1
Due: Assignment 2
Due: Proposal
Due: Interim Report
Due: Final Report
Lecture slides and other materials can be downloaded from
http://www.mysmu.edu/faculty/hclau/is703.html before class.
All assignments are to be submitted in writing (hardcopy) in class on the week it is due.
All project deliverables are to be submitted in both hardcopy and email softcopy.
Grading:
Class participation 15%
Assignments 20%
Mid-term 25%
Term paper and presentation 40%
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.
Workload: Roughly 12 hours/week
Mode of instructions: Workshop style - lecture plus discussions, using texts and
research papers.
Term Paper for IS703
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:
 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 11: Interim Progress Report due
Week 14: Final Report due and Class presentation