Robust integer programming

Robust integer programming

We provide a complexity classification of four variants of robust integer programming when the underlying Graver basis is given. We discuss applications to robust multicommodity flows and multidimensional transportation, and describe an effective parametrization of robust integer programming.

Optimal robust non-unique probe selection using Integer Linear Programming

MOTIVATION Besides their prevalent use for analyzing gene expression, microarrays are an efficient tool for biological, medical and industrial applications due to their ability to assess the presence or absence of biological agents, the targets, in a sample. Given a collection of genetic sequences of targets one faces the challenge of finding short oligonucleotides, the probes, which allow dete...

Benders Decomposition Approach to Robust Mixed Integer Programming

DISCRETE TOMOGRAPHY AND FUZZY INTEGER PROGRAMMING

We study the problem of reconstructing binary images from four projections data in a fuzzy environment. Given the uncertainly projections,w e want to find a binary image that respects as best as possible these projections. We provide an iterative algorithm based on fuzzy integer programming and linear membership functions.

Integer Programming

A short introduction to Integer Programming (IP). Problems leading to IP models. Some modelling tricks and reformulations. Geometry of linear IP. TUM matrices. Brief notes on polyhedral analysis. Separation theory. Chvatal cut hierarchy, Gomory cuts, Disjunctive cuts, RLT cut hierarchy. Iterative methods: Branch-and-Bound, Cutting Plane, Branch-and-Cut, Branch-and-Price. Lower bounds: Lagrangia...