【摘要】：在工程項(xiàng)目中,多個(gè)目標(biāo)間的協(xié)調(diào)與管理是項(xiàng)目管理的一項(xiàng)重要內(nèi)容,對項(xiàng)目工期、成本與質(zhì)量等方面的目標(biāo)實(shí)現(xiàn)有著直接的影響。當(dāng)前工程項(xiàng)目投資呈現(xiàn)出規(guī)模大、周期長、面臨風(fēng)險(xiǎn)眾多等特點(diǎn),這對項(xiàng)目的多目標(biāo)協(xié)調(diào)管理提出了更高的要求。因此,對工程項(xiàng)目的各目標(biāo)進(jìn)行分析與優(yōu)化研究,是實(shí)現(xiàn)多目標(biāo)均衡協(xié)調(diào)的基礎(chǔ);而綜合考慮工程項(xiàng)目所面臨的風(fēng)險(xiǎn)性及其所處環(huán)境的動(dòng)態(tài)性,則可使目標(biāo)優(yōu)化更加符合實(shí)際情況,且為實(shí)際管理提供理論依據(jù)。本文以工程項(xiàng)目的多目標(biāo)優(yōu)化為研究對象,基于傳統(tǒng)的多目標(biāo)優(yōu)化理論,引入模糊集理論、效用理論、動(dòng)態(tài)優(yōu)化理論,通過參數(shù)模糊化、選取置信水平、多屬性效用函數(shù)評價(jià)及加入時(shí)間參數(shù)體現(xiàn)風(fēng)險(xiǎn)因素和動(dòng)態(tài)環(huán)境因素的影響,構(gòu)建了考慮風(fēng)險(xiǎn)因素與動(dòng)態(tài)環(huán)境因素的多目標(biāo)優(yōu)化一般模型;分析工程項(xiàng)目多目標(biāo)特點(diǎn)與相互關(guān)系,提出工期-成本均衡優(yōu)化函數(shù)與工期-質(zhì)量均衡優(yōu)化函數(shù),結(jié)合改進(jìn)的一般優(yōu)化模型構(gòu)建了考慮風(fēng)險(xiǎn)因素的工程項(xiàng)目動(dòng)態(tài)模糊多目標(biāo)優(yōu)化模型,并選取適用于求解動(dòng)態(tài)優(yōu)化問題的微粒群算法;在此基礎(chǔ)上,進(jìn)行了算例分析,在給定的項(xiàng)目工序關(guān)系與其他參數(shù)的條件下,分別以靜態(tài)和動(dòng)態(tài)視角進(jìn)行建模與求解,靜態(tài)模型求解對比分析不同置信水平所代表的決策風(fēng)險(xiǎn)大小對帕累托解的影響;動(dòng)態(tài)模型求解對比分析不同階段由于決策者偏好的不同對帕累托解產(chǎn)生的影響。算例結(jié)果表明,隨著置信水平的提高,帕累托解分布區(qū)域無明顯變化,但其分散程度與模糊程度降低,說明項(xiàng)目決策風(fēng)險(xiǎn)的減小使得最優(yōu)方案集變得更加清晰,有利于決策者對最優(yōu)方案的選擇;隨著項(xiàng)目階段的推進(jìn),帕累托解的分布有明顯變化,說明由于決策者目標(biāo)偏好由重視質(zhì)量逐漸變?yōu)橹匾暪て?最優(yōu)解在工期目標(biāo)上有更好的表現(xiàn);驗(yàn)證了本文所建立的考慮風(fēng)險(xiǎn)因素的工程項(xiàng)目多目標(biāo)動(dòng)態(tài)優(yōu)化模型的可行性與合理性。
[Abstract]:In engineering projects, the coordination and management of multiple objectives is an important part of project management, which has a direct impact on project duration, cost and quality. At present, the project investment has the characteristics of large scale, long period and numerous risks, which puts forward higher requirements for the multi-objective coordinated management of the project. Therefore, the analysis and optimization of the objectives of engineering projects is the basis of achieving multi-objective equilibrium and coordination. Considering the risks faced by the project and the dynamic nature of the environment, the optimization of objectives can be more in line with the actual situation, and provide a theoretical basis for the actual management. Based on the traditional multi-objective optimization theory, fuzzy set theory, utility theory and dynamic optimization theory are introduced in this paper. The multi-attribute utility function evaluation and adding time parameters reflect the influence of risk factors and dynamic environmental factors, and a general multi-objective optimization model considering risk factors and dynamic environmental factors is constructed. Based on the analysis of the multi-objective characteristics and the relationship of engineering projects, the optimization function of time-cost equilibrium and the optimization function of time-quality equilibrium are proposed. Combined with the improved general optimization model, the dynamic fuzzy multi-objective optimization model of engineering project considering risk factors is constructed, and the particle swarm optimization algorithm suitable for solving dynamic optimization problem is selected. On the basis of this, an example analysis is carried out. Under the condition of given project working procedure relationship and other parameters, the model and solution are built and solved in the static and dynamic visual angle, respectively. The influence of the decision risk represented by different confidence levels on the Pareto solution is analyzed comparatively by solving the static model. The effects of different decision makers' preferences on the Pareto solution in different stages are analyzed. The numerical results show that with the increase of confidence level, the distribution region of Pareto solution does not change obviously, but the degree of dispersion and ambiguity decreases, which indicates that the reduction of project decision risk makes the optimal scheme set more clear. It is advantageous for the decision maker to choose the optimal scheme; With the development of the project stage, the distribution of Pareto solution changes obviously, which indicates that the optimal solution has a better performance on the target of the time limit because the preference of goal of decision maker gradually changes from emphasizing quality to attaching importance to time limit. The feasibility and rationality of the multi-objective dynamic optimization model with risk factors are verified.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU71

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 葉洪濤;羅飛;許玉格;;解決多目標(biāo)優(yōu)化問題的差分進(jìn)化算法研究進(jìn)展(英文)[J];控制理論與應(yīng)用;2013年07期

2 鄭歡;程周;;模糊多目標(biāo)規(guī)劃在建設(shè)項(xiàng)目決策中的應(yīng)用[J];統(tǒng)計(jì)與決策;2013年09期

3 張連營;岳巖;;工期—成本—質(zhì)量的模糊均衡優(yōu)化及其Pareto解[J];同濟(jì)大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年02期

4 李冬;王要武;宋暉;趙麗丹;;基于協(xié)同理論的政府投資項(xiàng)目跟蹤審計(jì)模式[J];系統(tǒng)工程理論與實(shí)踐;2013年02期

5 馬超;張松;李玉東;陳華平;;考慮風(fēng)險(xiǎn)的大型工程項(xiàng)目多目標(biāo)均衡優(yōu)化[J];計(jì)算機(jī)工程;2013年02期

6 郭研;李南;李興森;;多模式多資源均衡及基于動(dòng)態(tài)種群的多目標(biāo)微粒群算法[J];控制與決策;2013年01期

7 江善和;紀(jì)志成;張日東;沈艷霞;;基于種群年齡模型的動(dòng)態(tài)粒子數(shù)微粒群優(yōu)化算法[J];系統(tǒng)工程理論與實(shí)踐;2012年11期

8 王首元;孔淑紅;;新行為經(jīng)濟(jì)學(xué)理論:對期望效用理論和前景理論的一個(gè)延伸[J];西安交通大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2012年04期

9 逄金梅;鄭向偉;劉弘;;動(dòng)態(tài)微粒群算法及其在群體動(dòng)畫中的應(yīng)用[J];計(jì)算機(jī)工程與設(shè)計(jì);2011年08期

10 楊丁穎;黃建陵;;鐵路工程項(xiàng)目管理體系框架模型研究[J];科技進(jìn)步與對策;2011年13期

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