混合粒子群优化算法求解模糊柔性作业车间调度问题

Hybrid particle swarm optimization for solving fuzzy flexible job-shop scheduling problem

针对实际工厂中不确定加工时间的柔性作业车间调度问题,提出一种混合粒子群优化(HPSO)算法。用三角模糊数表示加工时间,以最小化最大模糊完工时间为优化目标建立数学模型。首先,在迭代过程中引入权重自适应调整策略,平衡算法的全局和局部搜索能力。其次,对优秀粒子进行交叉操作以产生更优个体,引入模拟退火算法增强深度寻优能力。最后,将所提算法运用于5个实例中进行仿真测试,并与粒子群优化(PSO)和改进人工蜂群等6种算法就模糊最大完工时间的平均值、最优值和最差值3项指标作对比。结果显示,HPSO求得的3项指标均优于或等于其余算法。在有限的运算资源条件下,HPSO求得的模糊最大完工时间整体小于PSO。随着实例数据量的增大,HPSO依然具有很好的求解稳定性。HPSO在一定程度上能够改善PSO易陷入局部最优的问题,且更适合求解模糊柔性作业车间调度问题。

A hybrid particle swarm optimization(HPSO)is proposed for the flexible job-shop scheduling problem of uncertain processing time in actual factories.The processing time is represented by a triangular fuzzy number,and a mathematical model is developed with the optimization goal of minimizing the maximum fuzzy completion time.Firstly,the adaptive adjustment strategy of weight is introduced in the iteration process to balance the global and local search capabilities of the algorithm.Secondly,cross operations are performed on excellent particles to produce better individuals,and simulated annealing is introduced to enhance the depth-seeking ability of the algorithm.Finally,the proposed algorithm is applied to five examples for simulation test,and compared with six algorithms,such as particle swarm optimization(PSO)algorithm and improved artificial bee colony algorithm,on average,best and worst of fuzzy maximum completion time.The results show that the three indexes obtained by HPSO are better than or equal to other algorithms.Under the condition of limited computing resources,the maximum fuzzy completion time obtained by HPSO is less than PSO.With the increase of case data,HPSO still has good stability.To a certain extent,HPSO can improve the problem that PSO is easy to fall into local optimum,and is more suitable for solving fuzzy flexible job-shop scheduling problem.