An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers

In this paper, an effective hybrid algorithm based on particle swarm optimization (HPSO) is proposed for permutation flow shop scheduling problem (PFSSP) with the limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). First, a novel encoding scheme based on random key representation is developed, which converts the continuous position values of particles in PSO to job permutations. Second, an efficient population initialization based on the famous Nawaz–Enscore–Ham (NEH) heuristic is proposed to generate an initial population with certain quality and diversity. Third, a local search strategy based on the generalization of the block elimination properties, named block-based local search, is probabilistically applied to some good particles. Moreover, simulated annealing (SA) with multi-neighborhood guided by an adaptive meta-Lamarckian learning strategy is designed to prevent the premature convergence and concentrate computing effort on promising solutions. Simulation results and comparisons demonstrate the effectiveness of the proposed HPSO. Furthermore, the effects of some parameters are discussed.