Improved shuffled frog leaping algorithm for solving multi-aisle automated warehouse scheduling optimization

Abstract

For multi-aisle automated warehouse scheduling optimization problem, a mathematical model with constraints is established, and a new shuffled frog leaping algorithm is proposed. During the process of obtaining optimal solution, to enhance the local search ability, stepsize is adjusted adaptively, and the frog individuals are guided to update. Meanwhile, in order to maintain the diversity of the populations and strengthen the global search ability, heuristic mutation operation is embedded. This not only ensures the global optimization, but also enhances the convergence efficiency. To verify the performance of the proposed algorithm, it is compared with shuffled frog leaping algorithm (SFLA) and genetic algorithm (GA) through simulation combined the industrial real case. Results show that the proposed algorithm achieves good performance in terms of the solution quality and the convergence efficiency. © Springer-Verlag Berlin Heidelberg 2013.