融合模拟退火参数的自适应遗传算法求解柔性作业车间调度问题
Adaptive Genetic Algorithm with Simulated Annealing Parameters for Solving Flexible Job-Shop Scheduling Problems
于琪 1张静1
作者信息
- 1. 西门子(中国)有限公司苏州分公司,江苏 苏州 215127
- 折叠
摘要
柔性作业车间调度问题是NP难问题,一般使用最大完工时间最短的评价指标来衡量加工顺序和机器选择的优劣,最短的完工时间意味着最快的生产速度.为了减小计算量并快速找到车间调度的最优解,提出了融合模拟退火参数的自适应遗传算法,详述了该算法的关键过程,并通过数据集的仿真实验验证了该算法的有效性.
Abstract
The flexible job-shop scheduling problem is an NP hard problem,which generally uses the shortest time for makespan time to measure the quality of processing sequence and machine selection.The shortest makespan time means the fastest production speed.In order to reduce computational complexity and quickly find the optimal solution for workshop scheduling,the paper proposes an Adaptive Genetic Algorithm with Simulated Annealing Parameters.The key process of the algorithm is detailed,and its effectiveness is verified through simulation experiments on a dataset.
关键词
作业调度/柔性作业/问题优化/自适应/模拟退火/遗传算法Key words
job scheduling/flexible operation/problem optimization/adaptive/simulated annealing/genetic algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据