In this paper, we propose an algorithm based on an artificial immune system to solve job shop scheduling problems. The approach uses clonal selection, hypermutations and a mechanism that explores the vicinity of a reference solution. It also uses a decoding strategy based on a search that tries to eliminate gaps in a schedule as to improve the solutions found so far. The proposed approach is compared with respect to three other heuristics using a standard benchmark available in the specialized literature. The results indicate that the proposed approach is very competitive with respect to the others against which it was compared. Our approach not only improves the overall results obtained by the other heuristics, but it also significantly reduces the CPU time required by at least one of them.
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