粒子群遗传算法在离散制造业排产中的研究

在离散制造业中,排产方法的优劣直接影响生产的效率.为了使算法更好的应用到排产当中,首先分析离散制造业的生产特点.同时,为了提高算法的搜索性能,分析遗传算法与粒子群优化算法的优缺点,提出了一种粒子群遗传混合算法(PSO_GA).该算法中,在遗传算法的基础上引入参数,从而动态控制每次迭代交叉变异比,进而提高群体多样性.同时为了克服遗传算法自身收敛速度慢的缺点,在适当的迭代周期内引入粒子群优化算法,从而提高算法的搜索速度和精度.最后,针对排产模型进行仿真实验,结果表明该算法具有很好的搜索性能.

Research on Particle Swarm Genetic Algorithm for Scheduling of Discrete Manufacturing Industry

In discrete manufacturing industry, the pros and cons of production scheduling method directly affects the efficiency of production. In order to make the algorithm be better applied to the production scheduling, characteristics of discrete manufacturing industry production are analyzed. At the same time, in order to improve the search performance of the algorithm, the advantages and disadvantages of genetic algorithm and particle swarm optimization algorithm are analyzed, and a PSO_GA hybrid algorithm is proposed. In this algorithm, introducing parameters on the basis of genetic algorithm, thus crossover and mutation are automatically controlled for each iteration. Then, population diversity is improved. In order to overcome the disadvantage of genetic algorithm with low convergence rate, particle swarm optimization algorithm is introduced in the appropriate iteration cycle, so as to improve the search speed and precision of the algorithm. Finally, the results of simulation experiments for production scheduling model verify the search performance of the algorithm.