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(xué)》2015年碩士論文　論文類型：學(xué)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(jì) 多目標(biāo)優(yōu)化 協(xié)同進(jìn)化

【摘要】：車間調(diào)度是通過(guò)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(biāo),它是制造企業(yè)提升自身市場(chǎng)競(jìng)爭(zhēng)力的關(guān)鍵因素。相對(duì)于傳統(tǒng)調(diào)度問(wèn)題,柔性作業(yè)車間調(diào)度問(wèn)題增加了加工機(jī)器柔性的特性,使其更貼近企業(yè)的現(xiàn)實(shí)生產(chǎn)模式,因而對(duì)它的研究更具實(shí)際應(yīng)用價(jià)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(duì)柔性作業(yè)車間調(diào)度中的單目標(biāo)和多目標(biāo)兩類問(wèn)題模型展開(kāi)討論,本文的主要工作概述如下：(1)對(duì)于柔性作業(yè)車間調(diào)度問(wèn)題,加工機(jī)器選擇子問(wèn)題的解決會(huì)影響到工序的加工順序子問(wèn)題的求解,反之亦然,因此兩個(gè)子問(wèn)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(gè)子問(wèn)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(biāo)柔性作業(yè)車間調(diào)度問(wèn)題時(shí),本文設(shè)計(jì)了一種基于分布估計(jì)的人工魚群算法,該算法是對(duì)基本人工魚群算法的一種改進(jìn)：為提高算法搜索的導(dǎo)向性設(shè)計(jì)了帶有分布估計(jì)能力的覓食行為,為加強(qiáng)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(kāi)發(fā)能力。使用160個(gè)經(jīng)典用例對(duì)提出的算法進(jìn)行實(shí)驗(yàn),通過(guò)與其他優(yōu)化算法地比較,證明了算法求解單目標(biāo)問(wèn)題的有效性。(3)針對(duì)最大完工時(shí)間、最大機(jī)器負(fù)載、總機(jī)器負(fù)載三個(gè)目標(biāo)的柔性作業(yè)車間調(diào)度模型,受協(xié)同進(jìn)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(guò)程中通過(guò)魚群的多種群協(xié)同進(jìn)行全局搜索,并與模擬退火算法協(xié)同增強(qiáng)局部搜索能力,另外針對(duì)多目標(biāo)問(wèn)題設(shè)計(jì)了改進(jìn)的ε—Pareto支配策略對(duì)適用度值進(jìn)行評(píng)價(jià),且在算法中采用擁擠距離和精英保留策略保持魚群中個(gè)體的多樣性；最后通過(guò)實(shí)驗(yàn)驗(yàn)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(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP18;TB497

【共引文獻(xiàn)】

相關(guān)期刊論文 前4條

1 吳秀麗;張志強(qiáng);杜彥華;閆瑾;;改進(jìn)細(xì)菌覓食算法求解柔性作業(yè)車間調(diào)度問(wèn)題[J];計(jì)算機(jī)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(hù)的聯(lián)合優(yōu)化研究[J];機(jī)械設(shè)計(jì)與制造;2015年07期

3 趙詩(shī)奎;;求解柔性作業(yè)車間調(diào)度問(wèn)題的兩級(jí)鄰域搜索混合算法[J];機(jī)械工程學(xué)報(bào);2015年14期

4 左益;公茂果;曾久琳;焦李成;;混合多目標(biāo)算法用于柔性作業(yè)車間調(diào)度問(wèn)題[J];計(jì)算機(jī)科學(xué);2015年09期

相關(guān)博士學(xué)位論文 前2條

1 趙詩(shī)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(xué);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(wèn)題研究[D];浙江工業(yè)大學(xué);2014年

相關(guān)碩士學(xué)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(jì)與實(shí)現(xiàn)[D];河北科技大學(xué);2013年

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

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本文編號(hào)：1342239