本文選題：遺傳算法 + 網(wǎng)絡(luò)計(jì)劃優(yōu)化��； 參考：《吉林大學(xué)》2013年碩士論文

【摘要】：隨著經(jīng)濟(jì)的發(fā)展，工程項(xiàng)目越來(lái)越大型化，復(fù)雜化，依靠傳統(tǒng)的方法已經(jīng)很難得到好的網(wǎng)絡(luò)決策計(jì)劃。網(wǎng)絡(luò)計(jì)劃優(yōu)化主要包括：工期優(yōu)化、費(fèi)用優(yōu)化、資源優(yōu)化三個(gè)方面。項(xiàng)目管理人員追求的目標(biāo)即是：安排合理的進(jìn)度計(jì)劃以使整個(gè)項(xiàng)目所花費(fèi)的資金最少、工期最短、資源最均衡，這是決定項(xiàng)目獲利與否以及獲利多少的關(guān)鍵。然而這些目標(biāo)的優(yōu)化問(wèn)題，目標(biāo)之間通常是相互沖突的，約束條件也是相沖突的，且目標(biāo)解不唯一，甚至不存在最優(yōu)解。項(xiàng)目決策優(yōu)化問(wèn)題的核心是網(wǎng)絡(luò)計(jì)劃技術(shù)，但解決這些問(wèn)題的傳統(tǒng)的數(shù)學(xué)規(guī)劃等方法存在很多缺陷如：方法針對(duì)性太強(qiáng)，，不能廣泛應(yīng)用于各實(shí)際問(wèn)題。在處理工作邏輯關(guān)系復(fù)雜的問(wèn)題時(shí)效率低，優(yōu)化效果大打折扣，所以為了更加科學(xué)、合理的進(jìn)行項(xiàng)目決策，本文選用智能化算法  遺傳算法來(lái)研究項(xiàng)目決策優(yōu)化問(wèn)題和網(wǎng)絡(luò)計(jì)劃優(yōu)化問(wèn)題。 遺傳算法，提供的是求解問(wèn)題的一種通用框架，具有較好的全局搜索性能，易于并行化。它可以用來(lái)有效地解決那些非線性的、不連續(xù)的、不可微不可導(dǎo)、多峰、多目標(biāo)的問(wèn)題，而且遺傳算法本身并不依賴(lài)于問(wèn)題的具體領(lǐng)域，非常適合處理離散優(yōu)化組合問(wèn)題，它具有很廣泛的可行解的表示，不需要輔助信息，具有群體搜索的特征和內(nèi)在的啟發(fā)式隨機(jī)搜索特征，而且可擴(kuò)展性高，易于和其他的方法結(jié)合使用，具有很高的魯棒性(Robust)，易于廣泛推廣使用。該算法在處理大型復(fù)雜系統(tǒng)優(yōu)化問(wèn)題上已經(jīng)取得了顯著的成果，其所表現(xiàn)出來(lái)的獨(dú)特的優(yōu)越性和健壯性，是其他方法所無(wú)法比擬的。 本文基于遺傳算法對(duì)項(xiàng)目工期 費(fèi)用優(yōu)化和工期 資源優(yōu)化分別進(jìn)行了應(yīng)用研究，基于時(shí)間 費(fèi)用的兩種常見(jiàn)關(guān)系類(lèi)型即連續(xù)型和離散型，分別設(shè)計(jì)了不同的遺傳算法優(yōu)化方法；對(duì)工期固定 資源均衡和資源有限 工期最短兩個(gè)問(wèn)題也分別建立了遺傳算法求解模型，并且在最后給出了算法實(shí)例進(jìn)行驗(yàn)證，得出優(yōu)化結(jié)果，得到一系列最優(yōu)解。本文在模型的構(gòu)建以及求解算法上的研究為項(xiàng)目管理中的目標(biāo)優(yōu)化問(wèn)題提供了一種新穎的可操作性強(qiáng)的思路與方法。項(xiàng)目管理人員可根據(jù)實(shí)際情況對(duì)優(yōu)化方案進(jìn)行比較選擇，達(dá)到提高經(jīng)濟(jì)效益的最終目的，具有極強(qiáng)的現(xiàn)實(shí)指導(dǎo)意義。
[Abstract]:With the development of economy, engineering projects are becoming more and more large-scale and complicated. It is difficult to get good network decision plan by traditional methods. Network planning optimization includes three aspects: duration optimization, cost optimization and resource optimization. The goal pursued by the project manager is to arrange a reasonable schedule to make the whole project spend the least amount of money, the shortest duration and the most balanced resources, which is the key to determine whether the project is profitable or not and how much. However, the optimization problems of these objectives are usually conflicting with each other, and the constraints are also conflicting, and the solution of the target is not unique, or even the optimal solution does not exist. The core of the project decision optimization problem is the network planning technology, but the traditional mathematical programming method to solve these problems has many defects, such as: the method is too targeted to be widely used in practical problems. In order to deal with the complex problem of working logic relationship, the efficiency is low, the optimization effect is greatly reduced, so in order to make the project decision more scientifically and reasonably, In this paper, an intelligent genetic algorithm is used to study project decision optimization and network planning optimization. Genetic algorithm, which is a general framework for solving problems, has good global search performance and is easy to be parallelized. It can be used to solve nonlinear, discontinuous, non-differentiable, multi-peak and multi-objective problems, and genetic algorithm itself is not dependent on the specific domain of the problem, so it is very suitable to deal with discrete optimization combinatorial problems. It has a wide range of representations of feasible solutions, does not require auxiliary information, has the characteristics of group search and inherent heuristic random search, and is highly scalable and easy to use in conjunction with other methods. It has high robustness and is easy to be widely used. The algorithm has achieved remarkable results in dealing with large-scale complex system optimization problems, and its unique superiority and robustness can not be compared with other methods. In this paper, based on genetic algorithm, the cost optimization and resource optimization of project are studied, and the two common relationship types of time cost are continuous and discrete. Different genetic algorithm optimization methods are designed respectively, and the solving models of genetic algorithm are established for the two problems of fixed time limit resource balance and resource limited time limit, and an example is given to verify the algorithm. The optimization results are obtained and a series of optimal solutions are obtained. In this paper, the construction of the model and the research of solving algorithm provide a novel and operable method for the goal optimization problem in project management. According to the actual situation, the project manager can compare and select the optimization scheme to achieve the ultimate goal of improving economic benefits, which has a strong practical significance.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TU712

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張厚先;;網(wǎng)絡(luò)計(jì)劃費(fèi)用優(yōu)化方法的討論[J];四川建筑科學(xué)研究;2007年01期

2 陸浩,吳喚群,羅毅;工期-費(fèi)用優(yōu)化數(shù)學(xué)模型及程序?qū)崿F(xiàn)[J];長(zhǎng)沙交通學(xué)院學(xué)報(bào);2001年02期

3 劉永建;趙勝利;劉燕;白永兵;杜光乾;于秋玲;;基于遺傳算法的網(wǎng)絡(luò)計(jì)劃資源優(yōu)化研究[J];河北農(nóng)業(yè)大學(xué)學(xué)報(bào);2007年05期

4 宋健海,劉士新,王夢(mèng)光,唐立新;鋼鐵企業(yè)產(chǎn)成品發(fā)貨裝船調(diào)度的模型與算法[J];東北大學(xué)學(xué)報(bào);2002年01期

5 李東南;陸衛(wèi)星;陳存恩;;遺傳算法在資源均衡問(wèn)題中的應(yīng)用[J];廣東科技;2007年08期

6 張連營(yíng),張金平,王亮;工程項(xiàng)目資源均衡的遺傳算法及其MATLAB實(shí)現(xiàn)[J];管理工程學(xué)報(bào);2004年01期

7 崔瑞濤;;利用工程網(wǎng)絡(luò)計(jì)劃對(duì)費(fèi)用和工期的優(yōu)化[J];廣西城鎮(zhèn)建設(shè);2007年01期

8 周康,同小軍,許進(jìn);資源優(yōu)化模型及遺傳算法[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2005年10期

9 黃凱明;莊鴻棉;;用改進(jìn)的遺傳算法實(shí)現(xiàn)網(wǎng)絡(luò)計(jì)劃優(yōu)化[J];集美大學(xué)學(xué)報(bào)(自然科學(xué)版);2006年03期

10 姚新，陳國(guó)良，徐惠敏，劉勇;進(jìn)化算法研究進(jìn)展[J];計(jì)算機(jī)學(xué)報(bào);1995年09期

相關(guān)碩士學(xué)位論文 前1條

1 阮宏博;基于遺傳算法的工程多目標(biāo)優(yōu)化研究[D];大連理工大學(xué);2007年

本文編號(hào)：2026875