A Surprisal-Based Greedy Heuristic for the Set Covering Problem

In this paper we exploit concepts from Information Theory to improve the classical Chvatal greedy algorithm for set covering problem. particular, develop a new procedure, called Surprisal-Based Greedy Heuristic (SBH), incorporating computation of “surprisal” measure when selecting solution columns. Computational experiments, performed on instances OR-Library, showed that SBH yields 2.5% improvement in terms objective function value over Chvatal’s while retaining similar execution times, making it suitable real-time applications. The heuristic was also compared with Kordalewski’s algorithm, obtaining solutions much shorter times large instances, and Grossmann Wool’s unicost where obtained better solutions.