  This paper deals with a robust recoverable approach to 0-1 programming
problems. It is assumed that a solution constructed in the first stage can be
modified to some extent in the second stage. This modification consists in
choosing a solution in some prescribed neighborhood of the current solution.
The second stage solution cost can be uncertain and a polyhedral structure of
uncertainty is used. The resulting robust recoverable problem is a min-max-min
problem, which can be hard to solve when the number of variables is large. In
this paper we provide a framework for solving robust recoverable 0-1
programming problems with a specified polyhedral uncertainty and propose
several lower bounds and approximate solutions, which can be used for a wide
class of 0-1 optimization problems. The results of computational tests for two
problems, namely the assignment and the knapsack ones, are also presented.
