本文選題：項(xiàng)目計劃與調(diào)度 + 協(xié)作計劃��； 參考：《揚(yáng)州大學(xué)》2017年碩士論文

【摘要】：網(wǎng)絡(luò)計劃技術(shù)是一套科學(xué)有效的項(xiàng)目管理方法,被廣泛應(yīng)用于工程項(xiàng)目計劃管理中。經(jīng)典項(xiàng)目計劃模型中,部分研究模型考慮了任務(wù)可拆分的協(xié)作計劃,而未引入任務(wù)可任意拆分的協(xié)作優(yōu)化機(jī)制;當(dāng)有多個協(xié)作伙伴同時競爭外協(xié)任務(wù)時,如何進(jìn)行協(xié)作伙伴選擇是一個復(fù)雜的組合優(yōu)化問題,需用合適的數(shù)學(xué)模型以及優(yōu)化方法,為協(xié)作伙伴選擇提供量化計算方法,提高決策效率;研究網(wǎng)絡(luò)計劃時需同步考慮資源約束,構(gòu)建計劃與調(diào)度的協(xié)同優(yōu)化機(jī)制;資源受限項(xiàng)目協(xié)作計劃與調(diào)度問題具備天然的多目標(biāo)特性,但部分研究仍局限于單目標(biāo)領(lǐng)域,或本質(zhì)仍將多目標(biāo)轉(zhuǎn)化為單目標(biāo)進(jìn)行優(yōu)化。因此,研究有競爭的多目標(biāo)資源受限項(xiàng)目協(xié)作計劃與調(diào)度具有重要的現(xiàn)實(shí)意義和理論價值。本文以項(xiàng)目計劃與調(diào)度為研究對象。首先建立有競爭的多目標(biāo)資源受限項(xiàng)目協(xié)作計劃與調(diào)度模型;其次建立有競爭的多目標(biāo)資源受限項(xiàng)目柔性協(xié)作計劃與調(diào)度模型;最后設(shè)計了 NSGA-Ⅱ算法以及多目標(biāo)差分進(jìn)化算法分別對以上兩種模型進(jìn)行求解。1.建立有競爭的多目標(biāo)資源受限項(xiàng)目協(xié)作計劃與調(diào)度模型。考慮有協(xié)作的計劃與調(diào)度,針對任務(wù)有條件拆分外協(xié)的情形,以關(guān)鍵資源為約束,構(gòu)建了以項(xiàng)目成本和完工周期為優(yōu)化目標(biāo)的有競爭的多目標(biāo)資源受限項(xiàng)目協(xié)作計劃與調(diào)度模型。該模型引入有競爭的協(xié)作計劃,即有多個協(xié)作伙伴同時競爭外協(xié)任務(wù),建立選擇協(xié)作伙伴的新型決策機(jī)制,以及基于任務(wù)可拆分的協(xié)作優(yōu)化機(jī)制,為關(guān)鍵資源調(diào)度和協(xié)作伙伴選擇提供理論支撐,提高資源利用率和選擇協(xié)作伙伴決策效率。2.建立有競爭的多目標(biāo)資源受限項(xiàng)目柔性協(xié)作計劃與調(diào)度模型。針對任務(wù)可任意拆分外協(xié)的情形,構(gòu)建了以項(xiàng)目成本和完工周期為優(yōu)化目標(biāo)的有競爭的多目標(biāo)資源受限項(xiàng)目柔性協(xié)作計劃與調(diào)度模型,設(shè)計了滿足任務(wù)可任意拆分要求的協(xié)作染色體編碼方案,針對模型的特殊性,運(yùn)用多目標(biāo)差分進(jìn)化算法對其進(jìn)行求解。最后,用工程實(shí)例進(jìn)行仿真對比。結(jié)果表明,制定基于任務(wù)可任意拆分的柔性協(xié)作計劃,可使協(xié)作比例更為合理,求解出的項(xiàng)目成本和完工周期更優(yōu)。3.設(shè)計了基于Pareto最優(yōu)的多目標(biāo)求解算法。為實(shí)現(xiàn)多目標(biāo)同步優(yōu)化,引入Pareto最優(yōu)機(jī)制,提出了基于Pareto的以NSGA-II和多目標(biāo)差分進(jìn)化算法為基礎(chǔ)的多目標(biāo)進(jìn)化算法。針對有競爭的協(xié)作計劃中任務(wù)可拆分外協(xié)以及關(guān)鍵資源約束等問題,設(shè)計了包含優(yōu)先級染色體、協(xié)作染色體和協(xié)作伙伴染色體的三維染色體編碼方案。在此基礎(chǔ)上,針對柔性協(xié)作計劃,改進(jìn)了協(xié)作染色體編碼方案。同時根據(jù)各模型的特殊性,設(shè)計了相應(yīng)的選擇、交叉、變異等進(jìn)化操作,并利用兩種算法各自不同的特性求解本文中兩個模型。最后,通過模擬一個工程實(shí)例,證明了模型及算法的有效性。
[Abstract]:Network planning technology is a scientific and effective project management method, which is widely used in project planning management. In the classic project planning model, some of the research models take into account the collaboration plan that can be disassembled by tasks without introducing a cooperative optimization mechanism that can be arbitrarily split into the task. How to select cooperative partners is a complex combinatorial optimization problem. It is necessary to use appropriate mathematical models and optimization methods to provide a quantitative calculation method for partner selection and improve decision-making efficiency. In the study of network planning, resource constraints should be considered synchronously and cooperative optimization mechanism for planning and scheduling is constructed; resource limited project collaboration plan is used. The problem of scheduling and scheduling has natural multi-objective characteristics, but some research is still limited to the single target field, or the multi target is still transformed into a single target in essence. Therefore, it is of great practical significance and theoretical value to study the cooperative planning and scheduling of a competitive multi target resource limited project. In the first place, a competitive multi target resource constrained project collaboration plan and scheduling model are established. Secondly, a competitive multi-objective resource constrained project flexible collaboration plan and scheduling model is established. Finally, the NSGA- II algorithm and multi-objective differential evolution algorithm are designed to solve the above two models to establish a competitive.1.. A multi objective resource constrained project collaboration plan and scheduling model. Considering the cooperative planning and scheduling, the situation of the conditional separation of the tasks and the constraints of the key resources, a competitive multi-objective resource constrained project collaboration plan and scheduling model is constructed with the project cost and completion cycle as the optimization goal. The competitive collaboration plan, that is, a number of partners compete with the collaborative task, establish a new decision mechanism to select partners, and a cooperative optimization mechanism based on task resolution, to provide theoretical support for key resource scheduling and partner selection, to improve the efficiency of resource utilization and to select the efficiency of cooperative partner decision making.2.. The flexible collaboration plan and scheduling model of multi target resource constrained project. Based on the case that the task can be arbitrarily disassembled, a competitive multi-objective resource constrained project flexible collaboration plan and scheduling model with the objective of the project cost and completion period is constructed, and a cooperative chromosome is designed for the arbitrary task to be disassembled at any level. In the light of the particularity of the model, the multi-objective differential evolution algorithm is used to solve it. Finally, the simulation comparison is made with an engineering example. The results show that a flexible collaboration plan based on the arbitrary resolution of the task can make the cooperative proportion more reasonable, the project cost and the completion period are better.3. design based on the Pareto In order to achieve multi-objective synchronization optimization and introduce the Pareto optimal mechanism, a multi-objective evolutionary algorithm based on the NSGA-II and multi-objective differential evolution algorithm based on Pareto is proposed to achieve multi objective synchronization optimization. The priority is designed to include the problems of task disassembly and key resource constraints in a competitive cooperative plan. A three-dimensional chromosome coding scheme for chromosomes, cooperative chromosomes and partner chromosomes. On this basis, the cooperative chromosome coding scheme is improved for the flexible collaboration plan. At the same time, according to the particularity of each model, the corresponding selection, cross, variation and other evolutionary exercises are designed, and the different characteristics of the two algorithms are used to solve this problem. Two models are presented in the paper. Finally, an engineering example is given to demonstrate the effectiveness of the model and algorithm.

【學(xué)位授予單位】：揚(yáng)州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TU72

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