本文選題：模擬退火算法 + 粒子群算法　； 參考：《山東師范大學(xué)》2017年碩士論文

【摘要】：隨著全球貿(mào)易的一體化,配置的全球化,顧客需求的多樣化,企業(yè)間的競爭越來越激烈,尤其是在制造領(lǐng)域,在這種環(huán)境下,企業(yè)為了生存并且在競爭中取得一定的話語權(quán),越來越重視對(duì)于生產(chǎn)的管理。流水車間是現(xiàn)如今企業(yè)采用最為廣泛的生產(chǎn)作業(yè)形式,對(duì)其研究對(duì)企業(yè)的生產(chǎn)具有重要的意義。流水車間調(diào)度的主要目標(biāo)便是根據(jù)企業(yè)的實(shí)際生產(chǎn)狀況,合理的安排生產(chǎn),使企業(yè)能夠達(dá)到所設(shè)定的目標(biāo),實(shí)現(xiàn)利潤的最大化。通過研究得知流水車間調(diào)度問題是一類典型的NP-Hard問題,求解過程太過復(fù)雜,研究者一直試圖尋求一種有效的算法能夠求解該問題,以便將其應(yīng)用于實(shí)際的生產(chǎn)過程中。本文主要通過改進(jìn)粒子群算法對(duì)置換和無等待的流水生產(chǎn)車間調(diào)度問題進(jìn)行求解。粒子群算法作為一種新的智能優(yōu)化算法,在搜尋的過程中,既受到個(gè)體搜尋的最優(yōu)位置的影響,也會(huì)受到群體的最優(yōu)位置的影響,具有快速的求解速度和較強(qiáng)的搜尋最優(yōu)解的能力。因而本文將其應(yīng)用于求解流水車間調(diào)度問題,但通過對(duì)粒子群的研究分析后發(fā)現(xiàn)粒子群算法主要應(yīng)用于求解連續(xù)的問題,而車間調(diào)度問題的最優(yōu)解是離散的,而且粒子群算法在應(yīng)用過程中容易過早收斂而陷入某一局部最優(yōu)的困境。對(duì)于粒子群的這些問題,本文為改進(jìn)粒子群算法提出了改進(jìn)的模擬退火粒子群算法和縱橫交叉粒子群算法,并應(yīng)用于求解置換流水車間和無等待流水車間調(diào)度問題中,基本創(chuàng)新點(diǎn)如下:(1)在置換流水車間問題的求解過程中,鑒于模擬退火算法具有較強(qiáng)的擴(kuò)展搜尋范圍的能力,能夠跳出局部最優(yōu),將退火策略嵌入到種群粒子的更新過程中,構(gòu)成模擬退火粒子群算法,通過優(yōu)化種群的最優(yōu)解,使粒子群算法擺脫受限于某一局部最優(yōu)的困鏡,在優(yōu)化的過程中,采用交換、插入、逆序三種鄰域搜索機(jī)制,根據(jù)Metropolis接受準(zhǔn)則選取產(chǎn)生的解,并將改進(jìn)后的算法用于求解置換流水車間數(shù)學(xué)模型中。(2)在無等待流水車間問題的求解過程中,本文引入了最新提出的縱橫交叉算法對(duì)粒子群進(jìn)行了優(yōu)化,構(gòu)成縱橫交叉粒子群算法,主要依靠縱橫交叉的橫向交叉增強(qiáng)粒子之間的信息傳遞和縱向交叉跳出局部最優(yōu)的能力,采用嵌入式和串行式兩種結(jié)合方式,對(duì)粒子的歷史最優(yōu)位置進(jìn)行優(yōu)化,將不同結(jié)合方式的縱橫交叉粒子群算法應(yīng)用于求解典型的無等待流水車間調(diào)度問題,通過實(shí)驗(yàn)證明了嵌入式的縱橫交叉粒子群算法具有較好的性能。
[Abstract]:With the integration of global trade, the globalization of distribution, the diversification of customer demand, the competition between enterprises is becoming more and more fierce, especially in the field of manufacturing. In this environment, the enterprises in order to survive and in the competition to obtain a certain right of speech.More and more attention is paid to the management of production.The flow shop is the most widely used production form in enterprises nowadays, and the research on it is of great significance to the production of enterprises.The main goal of the flow shop scheduling is to arrange production reasonably according to the actual production condition of the enterprise, so that the enterprise can achieve the set goal and realize the maximization of profit.It is found that the flow shop scheduling problem is a typical NP-Hard problem and the solution process is too complex. Researchers have been trying to find an effective algorithm to solve the problem in order to apply it to the actual production process.In this paper, the improved particle swarm optimization (PSO) algorithm is used to solve the flow shop scheduling problem with permutation and no waiting.As a new intelligent optimization algorithm, particle swarm optimization (PSO) is not only affected by the optimal location of individual search, but also by the optimal location of population.It has fast solving speed and strong ability of searching for optimal solution.Therefore, this paper applies it to solve the flow shop scheduling problem, but through the research and analysis of particle swarm optimization, it is found that particle swarm optimization algorithm is mainly used to solve continuous problems, and the optimal solution of job shop scheduling problem is discrete.Particle swarm optimization (PSO) is easy to converge prematurely and fall into a local optimal dilemma.For these problems, this paper presents an improved simulated annealing particle swarm optimization algorithm and a longitudinal and horizontal crossover particle swarm optimization algorithm for the improved particle swarm optimization algorithm, and it is applied to solve the scheduling problem of permutation flow shop and waiting free flow shop.The basic innovation is as follows: (1) in the process of solving the replacement flow shop problem, in view of the strong ability of the simulated annealing algorithm to extend the search range, the simulated annealing algorithm can jump out of the local optimum and embed the annealing strategy into the updating process of the population particles.The simulated annealing particle swarm optimization algorithm is constructed. By optimizing the optimal solution of the population, the particle swarm optimization algorithm can get rid of the trapped mirror which is limited by a local optimum. In the process of optimization, three neighborhood search mechanisms, exchange, insert and inverse order, are adopted.According to the Metropolis acceptance criterion, the solution is selected, and the improved algorithm is used to solve the displacement flow shop mathematical model.In this paper, the newly proposed crosswise and vertical crossover algorithm is introduced to optimize the particle swarm optimization, which mainly depends on the horizontal and vertical crossover to enhance the information transfer between the particles and the ability of vertical crossover to jump out of the local optimum.In this paper, the historical optimal position of particles is optimized by using embedded and serial combination, and the crosswise particle swarm optimization (PSO) algorithm with different combination methods is applied to solve the typical job-shop scheduling problem without waiting.The experiments show that the embedded particle swarm optimization algorithm has good performance.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TB497

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