【摘要】：自動(dòng)導(dǎo)引車(chē)(Automated Guided Vehicle，AGV)是現(xiàn)代物流系統(tǒng)備受關(guān)注的關(guān)鍵設(shè)備。越來(lái)越多的大中型企業(yè)開(kāi)始應(yīng)用AGV系統(tǒng)，嘗試以自動(dòng)貨運(yùn)機(jī)器人來(lái)替代人工作業(yè)，節(jié)省人力資源成本，逐步實(shí)現(xiàn)工業(yè)自動(dòng)化。AGV系統(tǒng)運(yùn)行過(guò)程中積累了大量的無(wú)規(guī)則數(shù)據(jù)。如何利用數(shù)據(jù)挖掘技術(shù)對(duì)AGV系統(tǒng)的數(shù)據(jù)進(jìn)行有效的分析，從中提取出有用的信息，并利用這些信息提高AGV系統(tǒng)的運(yùn)行效率是一個(gè)值得研究的問(wèn)題。本文提出了一種基于遺傳算法的關(guān)聯(lián)規(guī)則方法，并對(duì)AGV系統(tǒng)中的數(shù)據(jù)進(jìn)行關(guān)聯(lián)分析。主要內(nèi)容如下： 闡述了數(shù)據(jù)挖掘、遺傳算法和關(guān)聯(lián)規(guī)則的相關(guān)知識(shí)。針對(duì)“支持度-置信度”關(guān)聯(lián)規(guī)則模型的不足，引入理解度和興趣度這兩個(gè)評(píng)價(jià)標(biāo)準(zhǔn),根據(jù)支持度、置信度、理解度和興趣度綜合評(píng)價(jià)一條關(guān)聯(lián)規(guī)則。 針對(duì)進(jìn)行關(guān)聯(lián)規(guī)則挖掘時(shí)，計(jì)算各個(gè)評(píng)價(jià)標(biāo)準(zhǔn)要重復(fù)掃描數(shù)據(jù)庫(kù)的問(wèn)題，提出了一種屬性目錄結(jié)構(gòu)，根據(jù)該結(jié)構(gòu)能夠有效地減少掃描數(shù)據(jù)庫(kù)的次數(shù)，從而減少關(guān)聯(lián)規(guī)則挖掘的時(shí)間。 根據(jù)遺傳算法的全局尋優(yōu)的特點(diǎn)，提出了一種基于遺傳算法的關(guān)聯(lián)規(guī)則算法，詳細(xì)介紹了該算法的染色體編碼方式，使用支持度、置信度、理解度和興趣度構(gòu)造適應(yīng)度函數(shù)并結(jié)合屬性目錄計(jì)算適應(yīng)值，產(chǎn)生初始群體，設(shè)計(jì)遺傳算子等方面。 最終將該算法應(yīng)用于AGV系統(tǒng)中，得到一些有價(jià)值的關(guān)聯(lián)規(guī)則，并將結(jié)果與其他算法進(jìn)行比較，，證明該算法的高效性。通過(guò)分析解釋這些規(guī)則，對(duì)AGV系統(tǒng)優(yōu)化，倉(cāng)庫(kù)貨物安排，貨物備貨量，工作人員分配等方面提供有價(jià)值的信息。
[Abstract]:Automatic guided vehicle (Automated Guided Vehicle,AGV) is the key equipment of modern logistics system. More and more large and medium-sized enterprises begin to apply AGV system, try to replace manual operation with automatic freight robot, save human resource cost and realize industrial automation step by step. AGV system accumulates a lot of irregular data in the process of operation. How to use data mining technology to effectively analyze the data of AGV system, extract useful information from it, and use this information to improve the operation efficiency of AGV system is a problem worthy of study. In this paper, an association rule method based on genetic algorithm is proposed, and the data in AGV system are analyzed. The main contents are as follows: the related knowledge of data mining, genetic algorithm and association rules is described. In view of the shortcomings of the "support-confidence" association rule model, two evaluation criteria, understanding degree and interest degree, are introduced to evaluate an association rule according to the degree of support, confidence, understanding and interest. In order to solve the problem that each evaluation standard should scan the database repeatedly when mining association rules, an attribute directory structure is proposed, according to which the number of scanning databases can be effectively reduced. In order to reduce the mining time of association rules. According to the characteristics of global optimization of genetic algorithm, an association rule algorithm based on genetic algorithm is proposed. The chromosome coding method, support degree and confidence level of the algorithm are introduced in detail. The fitness function is constructed by understanding degree and interest degree, and the fitness value is calculated by combining the attribute directory to generate the initial population, design genetic operator and so on. Finally, the algorithm is applied to AGV system, and some valuable association rules are obtained, and the results are compared with other algorithms to prove the efficiency of the algorithm. Through the analysis and interpretation of these rules, the optimization of AGV system, warehouse cargo arrangement, cargo reserve, staff distribution and other aspects of valuable information.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP311.13

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