Abstract
In this work we present a greedy randomized adaptive search procedure (GRASP)-based strategy for the set covering problem. The goal of this problem is to find a subset of columns from a zero-one matrix in order to cover all the rows with the minimal possible cost. The GRASP is a technique that through a sequential and finite number of steps constructs a solution using a set of simple randomized rules. Additionally, we also propose an iterated local search and reward/penalty procedures in order to improve the solutions found by the GRASP. Our approach has been tested using the well-known 65 non-unicost SCP benchmark instances from OR-library showing promising results.
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