【摘要】：在車(chē)輛調(diào)度建模時(shí),由于突發(fā)性和不規(guī)律性,無(wú)法形成穩(wěn)定的可預(yù)測(cè)狀態(tài)。傳統(tǒng)的車(chē)輛調(diào)度網(wǎng)絡(luò)中,缺少必要的預(yù)報(bào)機(jī)制,導(dǎo)致調(diào)度存在較大的滯后性,調(diào)度時(shí)間過(guò)長(zhǎng)。提出改進(jìn)經(jīng)緯格任務(wù)的遺傳算法,利用經(jīng)緯格性能體系結(jié)構(gòu)的預(yù)報(bào)機(jī)制,改良初始種群生成方式,提高遺傳算法衍生率減少迭代次數(shù),減少運(yùn)算提高運(yùn)行速度。對(duì)車(chē)輛調(diào)度任務(wù)提出了適應(yīng)度函數(shù),滿(mǎn)足改進(jìn)算法對(duì)調(diào)度任務(wù)的適應(yīng)度。實(shí)驗(yàn)結(jié)果表明,提出的改進(jìn)優(yōu)化經(jīng)緯格任務(wù)遺傳算法提高了調(diào)度的性能,更優(yōu)于傳統(tǒng)調(diào)度策略。
[Abstract]:In vehicle scheduling modeling, it is impossible to form a stable and predictable state due to sudden and irregular characteristics. In the traditional vehicle scheduling network, the lack of necessary forecasting mechanism leads to a large lag and long scheduling time. An improved genetic algorithm for warp and weft lattice tasks is proposed. By using the prediction mechanism of warp and weft lattice performance architecture, the initial population generation method is improved, the derivation rate of genetic algorithm is increased to reduce the number of iterations and the speed of operation is reduced. The fitness function of vehicle scheduling task is proposed to satisfy the fitness of the improved algorithm. The experimental results show that the proposed genetic algorithm improves the scheduling performance and is better than the traditional scheduling strategy.
【作者單位】： 中北大學(xué)機(jī)電工程學(xué)院;
【分類(lèi)號(hào)】：U492.22;TP18

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本文編號(hào)：2154049