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  本文關(guān)鍵詞：基于人工魚群算法的柔性作業(yè)車間調(diào)度研究　出處：《大連理工大學》2015年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 柔性作業(yè)車間調(diào)度 人工魚群算法 分布估計 多目標優(yōu)化 協(xié)同進化

【摘要】：車間調(diào)度是通過合理安排各種生產(chǎn)資源以滿足企業(yè)生產(chǎn)的某些性能指標,它是制造企業(yè)提升自身市場競爭力的關(guān)鍵因素。相對于傳統(tǒng)調(diào)度問題,柔性作業(yè)車間調(diào)度問題增加了加工機器柔性的特性,使其更貼近企業(yè)的現(xiàn)實生產(chǎn)模式,因而對它的研究更具實際應(yīng)用價值。本文以一種新型的群智能算法—人工魚群算法為基本優(yōu)化算法,分別針對柔性作業(yè)車間調(diào)度中的單目標和多目標兩類問題模型展開討論,本文的主要工作概述如下：(1)對于柔性作業(yè)車間調(diào)度問題,加工機器選擇子問題的解決會影響到工序的加工順序子問題的求解,反之亦然,因此兩個子問題之間是相互制約和相互影響的。本文提出了前置安排策略和后置安排策略,它們分別以不同的先后順序處理兩個子問題從而產(chǎn)生不同的調(diào)度方案,保證了種群的多樣性。(2)在求解單目標柔性作業(yè)車間調(diào)度問題時,本文設(shè)計了一種基于分布估計的人工魚群算法,該算法是對基本人工魚群算法的一種改進：為提高算法搜索的導向性設(shè)計了帶有分布估計能力的覓食行為,為加強算法的全局搜索能力提出了人工魚吸引行為,加入了基于關(guān)鍵路徑的局部搜索以均衡算法探索和開發(fā)能力。使用160個經(jīng)典用例對提出的算法進行實驗,通過與其他優(yōu)化算法地比較,證明了算法求解單目標問題的有效性。(3)針對最大完工時間、最大機器負載、總機器負載三個目標的柔性作業(yè)車間調(diào)度模型,受協(xié)同進化思想地啟發(fā),提出了一種協(xié)同混合人工魚群算法；該算法在求解過程中通過魚群的多種群協(xié)同進行全局搜索,并與模擬退火算法協(xié)同增強局部搜索能力,另外針對多目標問題設(shè)計了改進的ε—Pareto支配策略對適用度值進行評價,且在算法中采用擁擠距離和精英保留策略保持魚群中個體的多樣性；最后通過實驗驗證了該算法可以得到更優(yōu)質(zhì)的非劣解。
[Abstract]:Job shop scheduling is a key factor for manufacturing enterprises to enhance their market competitiveness by arranging various production resources to meet certain performance indicators of enterprises. Compared with traditional scheduling problem, flexible job shop scheduling problem increases the flexibility of machine processing, making it closer to the real production mode of enterprises, so the research on it is more practical. In this paper, the basic algorithm uses a novel swarm intelligence algorithm artificial fish swarm algorithm for the model, respectively, for the flexible job shop scheduling in single and multi objectives, two kinds of problems are discussed, an overview of the main work of this paper are as follows: (1) for the flexible job shop scheduling problem, machine selection method, the processing sequence of the sub problems will affect the process of the problem and vice versa, so between the two sub problems are interdependent and mutual influence. This paper puts forward the strategy of pre arrange and post arrange. They deal with two sub problems in different order, so as to generate different scheduling schemes and ensure the diversity of population. (2) in solving the multi-objective flexible job shop scheduling problem, this paper designs a kind of artificial fish swarm algorithm based on estimation of distribution, the algorithm is an improvement to the basic artificial fish swarm algorithm: design ability of foraging behavior with estimation of distribution oriented to improve the search algorithm, in order to strengthen the global search algorithm the ability to put forward the artificial fish attracting behavior, adding to the exploration and development of equalization algorithm based on local search ability of critical path. 160 classical use cases are used to experiment with the proposed algorithm, and the effectiveness of the algorithm is proved by comparing with other optimization algorithms. (3) for the flexible job shop scheduling model of the maximum completion time, the maximum machine load, the total machine load the three target, the idea of co evolution inspired, this paper proposes a collaborative hybrid artificial fish swarm algorithm; the algorithm through a variety of fish swarm CO with global search in the solution process, and simulated annealing algorithm enhance the ability of local searching, in addition to the multi-objective design e - Pareto improved control method to evaluate the fitness value, and the crowding distance and the elitist strategy to keep the diversity of the fish in the individual in the algorithm; it is proved by experiments that the algorithm can get better solution pareto.
【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP18;TB497

【共引文獻】

相關(guān)期刊論文 前4條

1 吳秀麗;張志強;杜彥華;閆瑾;;改進細菌覓食算法求解柔性作業(yè)車間調(diào)度問題[J];計算機集成制造系統(tǒng);2015年05期

2 馬慧民;葉健飛;;柔性車間調(diào)度與設(shè)備維護的聯(lián)合優(yōu)化研究[J];機械設(shè)計與制造;2015年07期

3 趙詩奎;;求解柔性作業(yè)車間調(diào)度問題的兩級鄰域搜索混合算法[J];機械工程學報;2015年14期

4 左益;公茂果;曾久琳;焦李成;;混合多目標算法用于柔性作業(yè)車間調(diào)度問題[J];計算機科學;2015年09期

相關(guān)博士學位論文 前2條

1 趙詩奎;基于遺傳算法的柔性資源調(diào)度優(yōu)化方法研究[D];浙江大學;2013年

2 張靜;基于混合離散粒子群算法的柔性作業(yè)車間調(diào)度問題研究[D];浙江工業(yè)大學;2014年

相關(guān)碩士學位論文 前2條

1 孫玉濤;基于遺傳算法的車間調(diào)度系統(tǒng)設(shè)計與實現(xiàn)[D];河北科技大學;2013年

2 霍禹嘉;基于改進的遺傳算法實現(xiàn)的車間調(diào)度系統(tǒng)[D];吉林大學;2015年

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本文編號：1342239