基于支持向量机的多载量小车实时调度

doi:10.13229/j.cnki.jdxbgxb201606036

摘要/Abstract

摘要： 为了有效地解决车辆装配系统中多载量小车的实时调度问题,提出了基于支持向量机的实时调度方法。首先对多载量小车的实时调度问题进行描述,同时建立以车辆装配线产量和物料搬运距离作为评价指标的目标函数。然后通过车辆装配线的物料搬运系统仿真生成样本离线训练支持向量机模型,在实时阶段利用支持向量机模型实现多载量小车“等待”或“搬运”的调度决策。试验结果表明,本文提出的方法明显优于最小批量法,其运行速度快、实时调度效果好,且对动态环境变化具有一定的自适应性,能够有效提升多载量小车的实时调度水平。

关键词: 人工智能, 多载量小车, 支持向量机, 实时调度, 物料搬运

Abstract: To tackle the real-time scheduling problem of multi-load carriers in automobile assembly lines, a real-time scheduling approach was proposed based on Support Vector Machine (SVM). First, the real-time scheduling problem of multi-load carriers is formally described, and an objective function of assembly line output and the delivery distance is set up. Then, samples are generated by the simulation of the assembly line with material handling system to train an SVM model offline. Finally, the trained SVM model is used in the real-time scheduling process to make “wait” or “deliver” decisions. Experimental results indicate that the proposed approach outperforms the Minimum Batch Size (MBS) approach. It runs fast, performs well and to some degree adapts to the changes in the dynamic environment. It can be effectively used to improve the real-time scheduling of multi-load carries.

Key words: artificial intelligence, multiple-load carriers, support vector machine(SVM), real-time scheduling, material handling

中图分类号:

TP29

周炳海, 徐佳惠. 基于支持向量机的多载量小车实时调度[J]. 吉林大学学报(工学版), 2016, 46(6): 2027-2033.

ZHOU Bing-hai, XU Jia-hui. SVM-based real-time scheduling approach of multi-load carries[J]. 吉林大学学报(工学版), 2016, 46(6): 2027-2033.

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