A clonal selection algorithm to minimize reshuffling in container stacking operations

A container is a widely used solution for the cargo storage to be transported between ports, playing a central role in international trade. Consequently, ships grew in size in order to maximize their container transportation capacity in each trip. Due to increasing demand, container terminals face the challenges of increasing their service capacity and optimizing the loading and unloading time of ships. This paper presents the proposal of a novel meta-heuristic based on the Clonal Selection Algorithm, named MRCLONALG, to minimize the number of reshuffles in operations involving piles of containers. The performance of the proposed model was evaluated through simulations and results compared with those obtained by algorithms from the literature under the same test conditions. The results show that MRCLONALG is competitive in terms of minimizing the need of reshuffles, besides presenting a reduced processing time compared with models of similar performance.