sequencing problem中文是什么意思

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中文翻譯
造句

日程計(jì)劃問(wèn)題

sequence n. 1.繼續(xù)；接續(xù)；連續(xù)。 2.順序；程序；次第；關(guān)系 ...
problem n. 1.問(wèn)題，課題；疑難問(wèn)題；令人困惑的情況。 2.【 ...
cscar sequencing problem 車(chē)輛排序問(wèn)題
job-shop sequencing problem 作業(yè)安排定序問(wèn)題
sequencing 測(cè)序，序列測(cè)定; 程序化; 次序安排; 定序; 排列程序，程序設(shè)計(jì); 排序; 序列分析
activity sequencing 活動(dòng)排序
address sequencing 地址排序
automatic sequencing 自動(dòng)測(cè)序; 自動(dòng)定序; 自動(dòng)排序
bisulfite sequencing 亞硫酸鹽定序
carbohydrate sequencing 糖測(cè)序; 糖定位; 糖作圖
confirmatory sequencing 確證性測(cè)序,證實(shí)性測(cè)序; 確證性測(cè)序，證實(shí)性測(cè)序
cycle sequencing 循環(huán)測(cè)序法
dideoxy sequencing 雙脫氧序列測(cè)定
direct sequencing 直接測(cè)序法
directed sequencing 定向測(cè)序
dna sequencing dna測(cè)序; dna定序; dna順序分析; 脫氧核糖核酸序列分析
gene sequencing 基因測(cè)序
general sequencing 常規(guī)序列
genomic sequencing 基因組測(cè)序
graphical sequencing 圖解順序
instructional sequencing 指令定序
logic sequencing 邏輯順序
machine sequencing 機(jī)器序列
multiprogramming sequencing 多道程序的定序; 多道程序定序
oligosaccharide sequencing 寡糖測(cè)序

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例句與用法

The optimal design for a kind of sequencing problem of n - set
元集的一類(lèi)排序問(wèn)題的最優(yōu)設(shè)計(jì)
The mathematical mode of a sequencing problem in management system is raised and it is solved successfully by designing the responding hga computer program
提出了生產(chǎn)管理中一類(lèi)排序問(wèn)題的數(shù)學(xué)模型并設(shè)計(jì)了有效的混合式遺傳算法求解程序。
The assignment problem is a type of sophisticated combinatory designs which are difficult to be solved by the traditional methods , and so is the sequencing problem
指派問(wèn)題和排序問(wèn)題是一類(lèi)復(fù)雜的組合問(wèn)題，傳統(tǒng)的方法難于解決。相比之下，遺傳算法解決此類(lèi)問(wèn)題具有優(yōu)勢(shì)。
Then , a restricted - aco ( ant colony optimization ) algorithm is proposed to solve the car - sequencing problem with buffer restriction under a round painted body storage , which reaches the objective to minimize the total conveyor stoppage time
在此基礎(chǔ)上，設(shè)計(jì)了約束集蟻群算法求解環(huán)型油漆車(chē)身緩沖區(qū)約束下，以傳送帶中斷時(shí)間最短為目標(biāo)的汽車(chē)制造排程問(wèn)題。
This paper mainly focuses on the air sequencing problem in terminal area . by means of the single - machine scheduling method , the practical problem is reformulated into the cumulative traveling salesman problem with ready times ( ctsr - rt )
本文主要針對(duì)終端區(qū)空中交通流量管理中的飛機(jī)排序問(wèn)題，運(yùn)用單機(jī)排序方法，將其轉(zhuǎn)化為一帶準(zhǔn)備時(shí)間的累積旅行商問(wèn)題( ctsp ? rt ) 。
By this algorithm , the optimal solution can been obtained in transportation problem n assignment problem , traveling salesman problenu flow shop sequencing problems hamilton problem . in addition , numerical examples have been given to demonstrate the actual applicant procedure
并且通過(guò)對(duì)排序問(wèn)題、貨郎擔(dān)貨問(wèn)題、指派問(wèn)題、 hamilton問(wèn)題等線(xiàn)形規(guī)劃問(wèn)題典型算例的求解，表明新的分配原則在一定程度上簡(jiǎn)化了原有的分配過(guò)程，具有更強(qiáng)的通用性。
We have found that many application of linear programming , such as assignment problem . , traveling salesman problem , flow shop sequencing problem , hamilton problem and so on , fall into the category of transportation problem , that is , of shipping at minimum total cost a homogeneous good from a set of m warehouses to a set of n markets
元素判別值分配法是基于運(yùn)輸問(wèn)題引發(fā)出的通用解法，通過(guò)遍歷調(diào)運(yùn)表中的各行各列計(jì)算出每個(gè)元素的分配優(yōu)先權(quán)重? ?元素判別值，然后依據(jù)元素判別值，根據(jù)調(diào)配原則進(jìn)行元素分配，使得分配方案在多數(shù)情況下一次分配即可獲得最優(yōu)解，但目前仍需要完善。
A modified genetic algorithm ( mga ) framework was developed and applied to the flowshop sequencing problems with objective of minimizing mean total flowtime . to improve the general genetic algorithm routine , two operations were introduced into the framework . firstly , the worst points were filtered off in each generation and replaced with the best individuals found in previous generations ; secondly , the most promising individual was selectively cultivating if a certain number of recent generations have not been improved yet . under conditions of flowshop machine , the initial population generation and crossover function can also be improved when the mga framework is implemented . computational experiments with random samples show that the mga is superior to general genetic algorithm in performance and comparable to special - purpose heuristic algorithms . the mga framework can also be easily extended to other optimizations even though it will be implemented differently in detail
提出了一個(gè)改進(jìn)遺傳算法的結(jié)構(gòu)，并且應(yīng)用于帶有目標(biāo)是最小平均總流程時(shí)間的流水調(diào)度排序中.為了改進(jìn)一般遺傳算法的程序，兩個(gè)新的操作被引進(jìn)到這個(gè)操作中.這兩個(gè)操作為： 1 )過(guò)濾操作：過(guò)濾掉在每一代中的最壞的個(gè)體，用前一代中的最好的個(gè)體替代它； 2 )培育操作：當(dāng)在一定代數(shù)內(nèi)算法不改進(jìn)時(shí)，選擇一個(gè)培育操作用于培育最有希望的個(gè)體.通過(guò)大量的隨機(jī)產(chǎn)生的問(wèn)題的例子的計(jì)算機(jī)實(shí)驗(yàn)顯示出，提出的算法的性能明顯好于一般遺傳算法，并且和此問(wèn)題的最好的專(zhuān)門(mén)意義的啟發(fā)式算法相匹配.新的mga框架很容易擴(kuò)展到其它最優(yōu)化當(dāng)中，只是實(shí)施的詳細(xì)的步驟有所不同

其他語(yǔ)種釋義

sequencing problemとは意味：順序問(wèn)題｛じゅんじょもんだい｝

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