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Spring 2007
"Experiencing the joy of learning"
Decision-making in an uncertain
world
INSTRUCTOR:
Dept: Industrial
and Systems Engineering
Office: 239C Zachry
Phone: 979-862-4066
Email: ntaimo@tamu.edu
CLASSROOM AND SCHEDULE:
Zachry 340
TR 11:10PM - 12:25PM
OFFICE HOURS:
TBA and by appointment (open door policy)
COURSE
DESCRIPTION
Introduction to models, theory and computational methods for large-scale
stochastic programming. Methods include decomposition algorithms for
large-scale mathematical programming such as Benders, regularized Benders,
Dantzig-Wolf, L-shaped and statistically motivated decomposition methods.
Applications, theory, practical algorithm implementation and computational
experimentation is emphasized.
PREREQUISITES
INEN 622 – Linear
Programming
STAT 610 or equivalent – Probability and
Statistics
C/C++ Programming “competence”
COURSE
OBJECTIVES
This is an introductory course to stochastic programming. The aim
of the course is to introduce students to optimal decision-making problems with
data uncertainty. The field of stochastic programming is currently developing
rapidly with contributions from many disciplines such as operations research,
mathematics, and probability. Stochastic programming has a wide range of
applications especially in science and engineering such as manufacturing,
transportation, telecommunications, electricity power generation, health care,
agriculture/forestry, finance, etc. The course will cover a broad overview of
the applications, basic theory, and decomposition methods of this vibrant field.
Emphasis in this course is placed on both theory and practical algorithm
implementation and tools for solving difficult stochastic programming problems.
This is a first course in stochastic programming and is suitable for students
with knowledge of linear programming, elementary analysis, probability, and
C/C++ programming. The programming skills are needed for algorithm
implementation using state-of-the-art optimization software CPLEX. This course
has a research level orientation and as such, students will be required to
review literature on stochastic programming and conduct computational
experiments with stochastic programs.
TEXTBOOK
AND ADDITIONAL COURSE MATERIAL
No course textbook is required. Course notes and other reading
material will be provided and posted on WebCT (eLearning). However, you
are encouraged to get a copy of a stochastic programming textbook for your
reference.
REFERENCES:
1. J.R. Birge and F. Louveaux, Introduction to
Stochastic Programming, 1st Edition, Duxbury Press, Belmont, CA, 2003. ISBN 0
534 35964 7.
2. A. Ruszczynski and A. Shapiro (Eds.),
Stochastic Programming. Handbooks in Operations Research and Management Science
Volume 10. New York, NY, 2003. ISBN 0 444 50854 6.
3. R.K. Martin, Large Scale Linear and Integer
Optimization: A Unified Approach, Kluwer, 1999.
4. ILOG CPLEX 9.0, Online User’s Manual and
Reference Manual, ILOG, S.A. http://www.ilog.com/, 2003.
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