本文選題：阻尼自適應(yīng)粒子群算法 + 阻尼自適應(yīng)慣性權(quán)重　； 參考：《浙江大學(xué)》2011年碩士論文

【摘要】：項(xiàng)目調(diào)度問題是項(xiàng)目管理的重要內(nèi)容,對(duì)其理論和實(shí)現(xiàn)方法的研究具有重要的現(xiàn)實(shí)意義。本文結(jié)合企業(yè)項(xiàng)目調(diào)度需求,提出了一種阻尼自適應(yīng)粒子群算法,建立了資源動(dòng)態(tài)分配項(xiàng)目調(diào)度問題和資源動(dòng)態(tài)分配模糊項(xiàng)目調(diào)度問題的模型并進(jìn)行了求解,開發(fā)了項(xiàng)目調(diào)度原型系統(tǒng)。最后將本文的理論和方法應(yīng)用于實(shí)際注塑機(jī)開發(fā)項(xiàng)目的調(diào)度中,取得了良好的效果。 全文的內(nèi)容主要包括： 第1章介紹了項(xiàng)目調(diào)度問題的研究背景及意義,分析了現(xiàn)有算法和模型中存在的問題,給出了全文主要研究?jī)?nèi)容和組織結(jié)構(gòu)。 第2章介紹了粒子群算法的研究概況,提出了一種阻尼自適應(yīng)粒子群算法。該算法中,針對(duì)粒子群算法全局和局部搜索能力的平衡問題,對(duì)阻尼運(yùn)動(dòng)的模型加以改進(jìn),提出了阻尼自適應(yīng)慣性權(quán)重周期性衰減的非線性改變策略；針對(duì)粒子群算法容易出現(xiàn)早熟收斂問題,提出了基于粒子群平均空間距離的自適應(yīng)變異策略。 第3章介紹了經(jīng)典資源受限項(xiàng)目調(diào)度問題的概況,針對(duì)傳統(tǒng)任務(wù)資源固定分配難以實(shí)現(xiàn)動(dòng)態(tài)與高效調(diào)度,提出了資源動(dòng)態(tài)分配策略：允許任務(wù)在資源未全部就緒時(shí)可以啟動(dòng),任務(wù)調(diào)度期間可隨資源的使用情況動(dòng)態(tài)調(diào)整。給出了資源閥值的定義和當(dāng)量工期的計(jì)算方法,建立了資源動(dòng)態(tài)分配項(xiàng)目調(diào)度問題的數(shù)學(xué)模型,分析了該模型縮短工期的條件。將資源動(dòng)態(tài)分配策略引入模糊項(xiàng)目調(diào)度問題中,建立了基于模糊工期的資源動(dòng)態(tài)分配項(xiàng)目調(diào)度問題數(shù)學(xué)模型,模糊工期采用六點(diǎn)模糊數(shù)表示。分別對(duì)串行調(diào)度產(chǎn)生方案和并行調(diào)度產(chǎn)生方案進(jìn)行了改進(jìn),以適應(yīng)引入資源動(dòng)態(tài)分配策略的項(xiàng)目調(diào)度問題。 第4章本章將阻尼自適應(yīng)粒子群算法用于資源動(dòng)態(tài)分配項(xiàng)目調(diào)度模型和資源動(dòng)態(tài)分配模糊項(xiàng)目調(diào)度模型的求解。提出了一種帶有定界概率和定界規(guī)則的任務(wù)鏈表的粒子編碼方法,采用基于優(yōu)先規(guī)則和隨機(jī)數(shù)的混合策略生成初始種群,提出了不變位交叉法對(duì)粒子實(shí)施更新、變異位領(lǐng)域?qū)αＷ訉?shí)施變異,保證了粒子更新、變異后的可行性。對(duì)通用測(cè)試庫(kù)和典型實(shí)例進(jìn)行了測(cè)試,比較了不同粒子編碼方法、不同資源水平和不同算法的求解效果,結(jié)果表明資源動(dòng)態(tài)分配策略和阻尼自適應(yīng)粒子群算法能夠有效的利用資源,縮短項(xiàng)目工期。 第5章開發(fā)了項(xiàng)目調(diào)度原型系統(tǒng),給出了該系統(tǒng)的體系結(jié)構(gòu)及功能模塊,并將該系統(tǒng)應(yīng)用于一個(gè)具體型號(hào)注塑機(jī)的調(diào)度過程,分析了各個(gè)模型的調(diào)度結(jié)果,得出本文提出的資源動(dòng)態(tài)分配的調(diào)度策略和算法改進(jìn)策略能夠充分利用資源,有效的縮短項(xiàng)目工期。該系統(tǒng)在企業(yè)得到成功運(yùn)行和應(yīng)用。 第6章總結(jié)本課題的主要研究?jī)?nèi)容和成果,展望了今后的研究方向。
[Abstract]:Project scheduling is an important part of project management.In this paper, a damped adaptive particle swarm optimization algorithm is proposed to meet the requirements of enterprise project scheduling. The models of resource dynamic allocation project scheduling problem and resource dynamic allocation fuzzy project scheduling problem are established and solved.A prototype project scheduling system is developed.Finally, the theory and method of this paper are applied to the scheduling of practical injection molding machine development project, and good results are obtained.The main contents of this paper are:Chapter 1 introduces the research background and significance of the project scheduling problem, analyzes the existing problems in the algorithms and models, and gives the main research content and organization structure.Chapter 2 introduces the research situation of particle swarm optimization and proposes a damping adaptive particle swarm optimization algorithm.In order to balance the global and local search ability of PSO, the model of damping motion is improved, and the nonlinear change strategy of damping adaptive inertial weight periodic attenuation is proposed.Aiming at the problem of premature convergence in particle swarm optimization (PSO), an adaptive mutation strategy based on the average space distance of PSO is proposed.Chapter 3 introduces the general situation of the classical resource-constrained project scheduling problem. In view of the difficulty of dynamic and efficient scheduling in traditional task resource allocation, a dynamic resource allocation strategy is proposed, which allows tasks to start when all resources are not ready.Task scheduling can be dynamically adjusted as resources are used.The definition of resource threshold and the calculation method of equivalent duration are given. The mathematical model of resource dynamic allocation project scheduling problem is established, and the conditions for shortening the time limit are analyzed.The dynamic resource allocation strategy is introduced into the fuzzy project scheduling problem, and the mathematical model of the resource dynamic allocation project scheduling problem based on the fuzzy duration is established. The fuzzy duration is represented by six points fuzzy number.The serial scheduling generation scheme and the parallel scheduling generation scheme are improved to adapt to the project scheduling problem with dynamic resource allocation strategy.In chapter 4, the damped adaptive particle swarm optimization algorithm is used to solve the scheduling model of resource dynamic allocation project and the fuzzy project scheduling model of resource dynamic allocation.In this paper, a particle coding method for task linked list with bound probability and bound rule is proposed. The hybrid strategy based on priority rule and random number is used to generate the initial population, and the invariant crossover method is proposed to update the particle.The mutation site domain implements the mutation to the particle, guarantees the particle renewal, after the mutation feasibility.The common test library and typical examples are tested, and the results of different particle coding methods, different resource levels and different algorithms are compared.The results show that the dynamic resource allocation strategy and the damping adaptive particle swarm optimization algorithm can effectively utilize the resources and shorten the project duration.In chapter 5, the prototype system of project scheduling is developed, and the architecture and function module of the system are given. The system is applied to the scheduling process of a specific injection molding machine, and the scheduling results of each model are analyzed.It is concluded that the scheduling strategy and algorithm improvement strategy proposed in this paper can make full use of resources and effectively shorten the project duration.The system has been successfully run and applied in enterprises.Chapter 6 summarizes the main research contents and results of this subject, and looks forward to the future research direction.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH186

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