基于双层粒子群优化算法的柔性作业车间调度优化

Flexible job-shop scheduling optimization based on two-layer particle swarm optimization algorithm

针对柔性作业车间调度问题(FJSP),提出了一种改进的双层粒子群优化(ITLPSO)算法。首先,以机器的最大完工时间最小化为优化目标,建立了一个柔性作业车间调度模型;然后,介绍了改进的双层PSO算法,为了避免陷入局部最优和提高收敛速度,算法中加入了停滞阻止策略和凹函数递减策略;最后,对相关实例进行求解,并与已有算法作了比较。实验结果表明,与标准PSO算法和双层粒子群优化(TLPSO)算法相比,最大完工时间的最优值分别减少了11和6,最大完工时间的平均值分别减少了15.7和4,收敛速度明显提高。经过性能分析,所提算法可以明显提高柔性作业车间的调度效率,从而获得了更优的调度方案。

To deal with the Flexible Job-shop Scbeduling Problem （FJSP）, an Improved Two-Layer Particle Swarm Optimization （ITLPSO） algorithm was proposed. First, minimization of the maximal completion time of all machines was taken as the optimization objective to establish a flexible job-shop scheduling model. And then the improved two-layer PSO algorithm was presented, in which the stagnation prevention strategy and concave function decreasing strategy were adopted to avoid falling into local optimum and to improve the converger：ce rate. Finally, the proposed algorithm was adopted to solve the relevant instance and the comparison with existing methods was also performed. The experimental results showed that, compared with the standard PSO algorithm and the Two-Layer Particle Swarm Optimization （TLPSO） algorithm, the optimal value of the maximum completion time was reduced by 11 and 6 respectively, the average maximum completion time was reduced by 15.7 and 4 respectively, and the eonvergene,e rate was improved obviously. The performance analysis shows that the proposed algorithm can improve the efficiency of the flexible job-shop scheduling obviously and obtain better scheduling solution.