本文選題：人工智能 + 多載量小車�。� 參考：《吉林大學學報(工學版)》2016年06期

【摘要】：為了有效地解決車輛裝配系統(tǒng)中多載量小車的實時調(diào)度問題,提出了基于支持向量機的實時調(diào)度方法。首先對多載量小車的實時調(diào)度問題進行描述,同時建立以車輛裝配線產(chǎn)量和物料搬運距離作為評價指標的目標函數(shù)。然后通過車輛裝配線的物料搬運系統(tǒng)仿真生成樣本離線訓練支持向量機模型,在實時階段利用支持向量機模型實現(xiàn)多載量小車"等待"或"搬運"的調(diào)度決策。試驗結果表明,本文提出的方法明顯優(yōu)于最小批量法,其運行速度快、實時調(diào)度效果好,且對動態(tài)環(huán)境變化具有一定的自適應性,能夠有效提升多載量小車的實時調(diào)度水平。
[Abstract]:In order to effectively solve the real-time scheduling problem of multi-load vehicles in vehicle assembly system, a real-time scheduling method based on support vector machine (SVM) is proposed. Firstly, the real-time scheduling problem of multi-load vehicles is described, and the objective function of vehicle assembly line production and material handling distance is established. Then the sample off-line training support vector machine model is generated by the material handling system simulation of the vehicle assembly line. The support vector machine model is used to realize the scheduling decision of the multi-load vehicle "waiting" or "moving" in the real time stage. The experimental results show that the proposed method is obviously superior to the minimum batch method, its running speed is fast, the real-time scheduling effect is good, and it has certain adaptability to the dynamic environment change, and it can effectively improve the real-time scheduling level of multi-load vehicles.
【作者單位】： 同濟大學機械與能源工程學院;
【基金】：國家自然科學基金項目(71471135;61273035)
【分類號】：U468.2;TP181

【相似文獻】

相關期刊論文 前1條

1 朱琴躍;謝維達;譚喜堂;趙亞輝;;MVB周期信息的實時調(diào)度[J];計算機應用;2007年12期

相關碩士學位論文 前1條

1 謝夢如;FlexRay總線系統(tǒng)實時調(diào)度方法研究與實現(xiàn)[D];合肥工業(yè)大學;2014年

，

本文編號：1946557