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A Quick-and-Dirty Approach to Robustness in Linear Optimization

We introduce methods for dealing with linear programming (LP) problems
with uncertain data, using the notion of weighted analytic centers.
Our methods are based on high interaction with the decision maker (DM) and try to
find solutions which satisfy most of his/her important criteria/goals.
Starting with the drawbacks of different methods for dealing with
uncertainty in LP, we explain how our methods improve most of them. We prove
that, besides many practical advantages, our approach is theoretically
as strong as robust optimization. Interactive cutting-plane algorithms are
developed for concave and quasi-concave utility functions. We present some
probabilistic bounds for feasibility and evaluate our approach by means
of computational experiments.

Identiferoai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/7178
Date January 2012
CreatorsKarimi, Mehdi
Source SetsUniversity of Waterloo Electronic Theses Repository
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation

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