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 library of antibodies to construct solutions. It also uses a local selection mechanism that tries to eliminate gaps between jobs in order to improve solutions produced by the search mechanism of the algorithm. The proposed approach is compared with respect to GRASP (an enumerative approach) in several test problems taken from the specialized literature. Our results indicate that the proposed algorithm is highly competitive, being able to produce better solutions than GRASP in several cases, at a fraction of its computational cost.
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