Selected Topics in Industrial Engineering I (IE 680)

2014 Spring
Faculty of Engineering and Natural Sciences
Industrial Engineering(IE)
3
10
Nilay Noyan nnoyan@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture,Interactive lecture
Discussion based learning,Project based learning,Other
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Programs\Type Required Core Elective Area Elective
Computer Science and Engineering - With Bachelor's Degree *
Computer Science and Engineering - With Master's Degree *
Computer Science and Engineering - With Thesis *
Cyber Security - With Bachelor's Degree *
Cyber Security - With Master's Degree *
Cyber Security - With Thesis *
Electronics Engineering and Computer Science - With Bachelor's Degree *
Electronics Engineering and Computer Science - With Master's Degree *
Electronics Engineering and Computer Science - With Thesis *
Electronics Engineering - With Bachelor's Degree *
Electronics Engineering - With Master's Degree *
Electronics Engineering - With Thesis *
Energy Technologies and Management-With Thesis *
Industrial Engineering - With Bachelor's Degree *
Industrial Engineering - With Master's Degree *
Industrial Engineering - With Thesis *
Leaders for Industry Biological Sciences and Bioengineering - Non Thesis *
Leaders for Industry Computer Science and Engineering - Non Thesis *
Leaders for Industry Electronics Engineering and Computer Science - Non Thesis *
Leaders for Industry Electronics Engineering - Non Thesis *
Leaders for Industry Industrial Engineering - Non Thesis *
Leaders for Industry Materials Science and Engineering - Non Thesis *
Leaders for Industry Mechatronics Engineering - Non Thesis *
Manufacturing Engineering - Non Thesis *
Manufacturing Engineering - With Bachelor's Degree *
Manufacturing Engineering - With Master's Degree *
Manufacturing Engineering - With Thesis *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) *
Mechatronics Engineering - With Bachelor's Degree *
Mechatronics Engineering - With Master's Degree *
Mechatronics Engineering - With Thesis *
Molecular Biology, Genetics and Bioengineering (Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology, Genetics and Bioengineering-(Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) *
Physics - Non Thesis *
Physics - With Bachelor's Degree
Physics - With Master's Degree

CONTENT

OBJECTIVE

The main objectives of this course are
* to review the basic concepts and challenges of stochastic programming;
* to review the main approaches to model decision-making problems in the presence of uncertainty and solve such stochastic optimization models;
* to discuss the strengths and weaknesses of the available modeling approaches and solution methods;

LEARNING OUTCOMES

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Midterm 35
Individual Project 35
Presentation 10
Homework 20

RECOMENDED or REQUIRED READINGS

Textbook

There is no required textbook. We will use parts of some references listed below. Some other reading assignments (lecture notes and relevant journal articles) will also be provided.


Supplementary Books:

1. Shapiro, A., Dentcheva, D. and A. Ruszczynski, Lectures on Stochastic Programming: Modeling and Theory. Society of Industrial and Applied Mathematics (SIAM), Philadelphia, 2009. The first edition can be found at: http://www.rusz.rutgers.edu/

2. Birge, J.R. and F. Louveaux, Introduction to Stochastic Programming. 2nd edition. Springer Series in Operations Research and Financial Engineering, New York, NY, 2011.

3. Kall, P. and S.W. Wallace, Stochastic Programming, John Wiley & Sons, Chichester, 1994. This book is available online free of charge. A copy can be found at: http://www.lancs-initiative.ac.uk/page/153/Resources.htm

4. Prékopa A. Stochastic programming. Dordrecht, Boston: Kluwer Academic, 1995.

5. Ruszczynski, A. and A. Shapiro (Eds.), Handbooks in Operations Research and Management Science, Volume 10: Stochastic Programming, Elsevier, 2003.