Flexible Flow-shop Scheduling Approach Based on Hybrid Particle Swarm Optimization

This paper proposes to take the ability difference between parallel machines into account in flexible flow-shop scheduling problems (FFSP), and formulates an integer programming model firstly. A particle coding and decoding method based on particle swarm optimization (PSO) for FFSP is proposed, A new particle computation measure to scheduling problems is presented, a hybrid particle swarm optimization (HPSO) with self-adaptive inertia weight and mutation operator is designed. Numerous experiments are undertaken to assess the performance of the new HPSO, computational results show that the proposed algorithm outperforms the existing genetic algorithms, and the convergence curve indicates that the HPSO effectively improves the capacity of basic PSO.