摘要
针对中小型企业生产车间柔性作业调度问题,采用改进的遗传算法求解最优调度结果。将最大完工时间最小化作为调度目标,对经典遗传算法进行相应的改进。首先利用粒子群算法获取工序序列与粒子参数之间的映射关系,在初始种群中利用混沌映射和反向学习策略以提高初始种群质量;然后提出一种将机器编码和工序编码相结合的分段编码方法,以解决某道工序有多台可选机器加工的问题;最后利用自适应交叉和变异概率提高算法收敛速度。通过对Brandimarte设计的10组不同规格的基准案例进行仿真实验,得到进化曲线和最优调度方案。实验结果验证了该方法的实用性和有效性。
In this paper,the flexible job scheduling problem of small and medium-sized enterprise production workshops is solved by using improved genetic algorithm to seek optimal scheduling results.By minimizing the maximum completion time as the scheduling tar⁃get,the classical genetic algorithm has been improved accordingly.Firstly,the particle swarm optimization algorithm is used to obtain the mapping relationship between process sequence and particle parameters.Chaotic map and reverse learning strategy are used in the initial population to improve the quality of the initial population.Then a sub-combination of machine coding and process coding is pro⁃posed.The segment coding method solves the problem of multiple optional machining in a certain process,and finally uses adaptive crossover and mutation probability to improve the convergence speed of the algorithm.Through the simulation experiments of 10 sets of benchmark cases designed by Brandimarte,the evolution curve and optimal scheduling scheme are obtained.The experimental results verify the practicability and effectiveness of the method.
作者
代招
李郝林
DAI Zhao;LI Hao-lin(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《软件导刊》
2020年第5期83-87,共5页
Software Guide
基金
上海市科学技术委员会基金项目(17DZ2283300)。
关键词
遗传算法
作业车间调度
柔性
完工时间
genetic algorithm
job shop scheduling problem
flexibility
makespan
