[1]张富强,白筠妍,张林朋.基于生产甘特图的 AGV 资源约束调度方法[J].郑州大学学报(工学版),2022,43(04):23-29.
 Scheduling Optimization Method of Limited AGV ba<x>sed on Production Gantt Chart[J].Journal of Zhengzhou University (Engineering Science),2022,43(04):23-29.
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基于生产甘特图的 AGV 资源约束调度方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
43
期数:
2022年04期
页码:
23-29
栏目:
出版日期:
2022-07-03

文章信息/Info

Title:
Scheduling Optimization Method of Limited AGV ba<x>sed on Production Gantt Chart
作者:
张富强白筠妍张林朋
文献标志码:
A
摘要:
面对制造企业数字化、网络化和智能化转型升级需求,自动引导车( AGV) 被广泛应用于生产作业的物流运输过程。 在对多品种小批量工件任务的工艺路线规划基础上,迫切需要对各加工运输环节进行集成以更加符合实际生产的要求。 针对有限 AGV 资源的柔性车间调度问题,构建了以最大完工时间、AGV 数量和资源不均衡率最小化的多目标模型, 采用基于生产甘特图的改进鲸鱼算法进行求解。首先,介绍了鲸鱼算法的基本原理;其次,设计了基于 AGV 数量、工序加工顺序和 AGV 编号的三段式编码方式将离散的数据转化为鲸鱼个体中的连续位置;最后,采用 3 种措施对算法进行改进:在初始化时通过反向学习策略获得较好的初始种群,而在迭代过程中分别加入自适应权重和变异因子,使算法的收 敛精度和全局搜索能力得到提高。 为验证算法的性能, 用改进鲸鱼算法与基本鲸鱼算法、 经典的NSGA-II 求解上述模型。 仿真结果表明,改进鲸鱼算法求解的质量较高且运行时间相较于 NSGA-II 缩短了 21. 6% 。 所提算法在有限 AGV 资源约束的智能化车间调度问题求解中有一定的实用价值。
Abstract:
At the demands of digital, networked and intelligent transformation and upgrading of manufacturing enterprises, automated guided vehicle ( AGV) is widely used in the logistics transportation of production oper- ation. On the basis of workpiece process route planning, it is urgent to plan the transportation in each process to meet the requirements of actual production. Aiming to solve the flexible job shop scheduling problem with limited AGV, a multi-objective scheduling model was established with the optimization functions of maximum completion time, AGV quantity and resource imbalance rate. An improved whale algorithm based on produc- tion Gantt chart was proposed to solve the above model. Firstly, the basic principle of whale algorithm was in- troduced. Secondly, a three-stage real number coding method including AGV quantity, process sequence and AGV number was designed to transform the discrete data into continuous positions in whale individuals. Then the algorithm was improved from three aspects. During initialization, a better initial population was obtained by reverse learning strategy; in the iterative process, adaptive weight and mutation factor were added separate- ly to improve the convergence accuracy and global search ability of the algorithm. Finally, the improved whale algorithm, the basic whale algorithm and the NSGA-II were used separately to solve the scheduling model. The simulation results showed that the improved whale algorithm had higher solution quality and the running time was 21. 6% shorter than NSGA-II. The algorithm proposed in the paper had certain practical value in solving the intelligent job shop scheduling problem with limited AGV resources.
更新日期/Last Update: 2022-07-03