To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of ...To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of product demands and the lim it of machine capacity,the existing layout needed to be rearranged to a high degree.Secondly,a mathematical model was established for the objective function of minimizing the total costs.Thirdly,a novel dynamic multi-swarm particle swarm optimization(DMS-PSO)algorithm based on the communication learning strategy(CLS)was developed.Toavoid falling into local optimum and slow convergence,each swarm shared their optimal locations before regrouping.Finally,simulation experiments were conducted under different conditions.Numerical results indicate that the proposed algorithm has better stability and it converges faster than other existing algorithms.展开更多
基金The National Natural Science Foundation of China(No.71471135)
文摘To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of product demands and the lim it of machine capacity,the existing layout needed to be rearranged to a high degree.Secondly,a mathematical model was established for the objective function of minimizing the total costs.Thirdly,a novel dynamic multi-swarm particle swarm optimization(DMS-PSO)algorithm based on the communication learning strategy(CLS)was developed.Toavoid falling into local optimum and slow convergence,each swarm shared their optimal locations before regrouping.Finally,simulation experiments were conducted under different conditions.Numerical results indicate that the proposed algorithm has better stability and it converges faster than other existing algorithms.
