stochastic programming

"stochastic programming"的翻譯和解釋

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This paper tries to study stochastic programming , especially chance constrained programming on the theory of probability and statistics
本文試圖利用概率統(tǒng)計(jì)有關(guān)理論作為工具，對(duì)隨機(jī)規(guī)劃特別是機(jī)會(huì)約束規(guī)劃進(jìn)行研究。
In this paper , we develop an asset and liability management model suited to the domestic circumstances according to the current situation in china based on the stochastic programming model for asset and liability management , in which we improve the subject restraints of the model
摘要在資產(chǎn)負(fù)債管理隨機(jī)規(guī)劃模型的基礎(chǔ)上，根據(jù)國內(nèi)實(shí)際情況，改進(jìn)了模型的約束條件，建立了適合國內(nèi)情況的資產(chǎn)負(fù)債管理模型。
Thus , according to mechanics of dealing with stochastic phenomena in programming theory , multi - objective stochastic programming model is developed to dispose parameter uncertainty . as a heuristic monte carlo approach with powerful global searching , genetic algorithm based on stochastic programming is utilized
為了更好的處理實(shí)際生產(chǎn)中參數(shù)的不確定性，根據(jù)數(shù)學(xué)規(guī)劃論中處理隨機(jī)現(xiàn)象的機(jī)理，建立多目標(biāo)隨機(jī)規(guī)劃模型，模型求解采用基于隨機(jī)規(guī)劃的遺傳算法。
Since the development of the simplex method many people have contributed to the growth of linear programming by developing its mathematical theory , devising efficient computational methods and codes , exploring new applications , and by their use of linear programming as an aiding tool for solving more complex problems , for instance , discrete programs , nonlinear programs , combinatorial problems , stochastic programming problems , and problems of optimal control
由于單純形的發(fā)展，很多人致力于線性規(guī)劃的進(jìn)步，通過發(fā)展它的數(shù)學(xué)理論、設(shè)計(jì)高效的計(jì)算方法和規(guī)則、探索新的應(yīng)用，以及把線性規(guī)劃的使用作為解決更復(fù)雜問題的輔助工具，比如，離散規(guī)劃，非線性規(guī)劃，組合問題，隨機(jī)規(guī)劃問題和最優(yōu)控制問題。
On the basis of analyzing the usual problems in the design of micro - irrigation , network for micro - irrigation is optimize designed . by the method of combining analysing the theories and the engineering examples and of stochastic programming , the network for micro - irrigation is optimize designed , and the stochatic non - linear programming model of micro - irrigation system is built
本文針對(duì)微灌工程規(guī)劃設(shè)計(jì)中的問題，在分析常規(guī)微灌系統(tǒng)規(guī)劃設(shè)計(jì)方法的基礎(chǔ)上，采用隨機(jī)規(guī)劃的數(shù)學(xué)方法，對(duì)微灌系統(tǒng)管網(wǎng)進(jìn)行了優(yōu)化設(shè)計(jì)。
Traditional uncertain programming mainly contains stochastic programming and fuzzy programming , which have many applications in manufacture , economy and management etc . especially , the theory of stochastic or fuzzy linear programming is more complete , so it has more applications than stochastic or fuzzy nonlinear programming
傳統(tǒng)的不確定規(guī)劃主要分為兩大類：隨機(jī)規(guī)劃和模糊規(guī)劃，并且在生產(chǎn)、經(jīng)濟(jì)及管理等諸多方面已有廣泛的應(yīng)用，特別是隨機(jī)線性規(guī)劃和模糊線性規(guī)劃理論較為完善，應(yīng)用更加廣泛。
In view of the fact that the genetic algorithm of stochastic programming based on random simulated technology has succeed greatly , this paper points out that changing parameters of genetic algorithm can obtain a sequence of optimum values of goal function . taking these genetic algorithm values as sampling data , we can get fitting optimum function by using multivariate spline regression and get the lipschitzs constant of the fitting optimum function . so for any chance constrained programming problem , we can get its interval estimate
鑒于基于隨機(jī)模擬技術(shù)的遺傳算法在求解隨機(jī)規(guī)劃問題上的優(yōu)越性，本文指出，改變遺傳算法的參數(shù)條件，在此基礎(chǔ)上求得機(jī)會(huì)約束規(guī)劃的若干個(gè)最優(yōu)值，以這些最優(yōu)值為樣本點(diǎn)，利用多元樣條回歸，擬合得到最優(yōu)值函數(shù)，進(jìn)而求出最優(yōu)值函數(shù)的lipschitzs常數(shù)，從而對(duì)于任一機(jī)會(huì)約束規(guī)劃問題，都可以得到它的一個(gè)區(qū)間估計(jì)。