混合蜂群算法求解柔性作业车间调度问题

Hybrid bee colony algorithm for flexible Job Shop scheduling problem

为解决柔性作业车间调度问题,提出一种基于蜂群模型的混合群智能优化算法。在算法初始化阶段提出了蜂群优化算法结合随机方法的种群初始化方法,提高了初始种群质量;为提高算法搜索精度,在观察蜂阶段采用模拟退火算法更新观察蜂群,并以退温系数调节邻域规模,随算法进程细化搜索范围;针对柔性作业车间调度问题特点,建立了可控规模的邻域更新方法。采用柔性作业车间标准算例,通过仿真编程和与其他算法的比较,验证了算法的有效性和优越性。

To solve the flexible Job Shop scheduling problem,a hybrid intelligent optimization algorithm based on bee colony model to improve the searching accuracy and efficiency was proposed.Combined bee colony optimization with stochastic methods,a novel method for generating initial population was put forward to improve initial population quality.To improve search accuracy,simulated annealing was used to update onlooker bees,and annealing coefficient was used to refine neighbor domains.Aiming at characteristics of flexible Job Shop scheduling problem,update method for neighborhood with controlled scale was established.By using flexible Job Shop standard algorithm,the validity and superiority of the algorithm was proved by comparing the simulation program with other algorithm.