本文選題：多準(zhǔn)則分類決策　切入點(diǎn)：偏好解聚　出處：《中南大學(xué)》2004年碩士論文　論文類型：學(xué)位論文

【摘要】：多準(zhǔn)則分類決策是多準(zhǔn)則決策的一個(gè)重要研究分支,是管理決策以及社會(huì)生活中常見的一類問題。簡(jiǎn)單來說,它處理的是多個(gè)方案在多個(gè)準(zhǔn)則下如何分門別類的問題。在實(shí)際決策中,方案的偏好值并不一定是各準(zhǔn)則偏好的線性組合,即準(zhǔn)則間并非完全獨(dú)立而是相關(guān)聯(lián)的,同時(shí)決策者也很難預(yù)先確定出所有偏好函數(shù)和權(quán)系數(shù),通常只能給出相應(yīng)的不完全信息。由此,本文針對(duì)這類不完全信息下非線性情況的多準(zhǔn)則決策問題展開了系統(tǒng)研究。 本文首先在查閱大量文獻(xiàn)的基礎(chǔ)上,論述了國(guó)內(nèi)外多準(zhǔn)則決策和分類方法的研究現(xiàn)狀,分析了目前已有方法,如MAUT、ELECTRE/PROMETHEE、UTA/UTADIS、M.H.DIS的局限性或不足。其次,改進(jìn)并構(gòu)建了具有非線性偏好聚合函數(shù)的多準(zhǔn)則分類優(yōu)化模型,并利用偏好解聚和層層遞進(jìn)的思想,進(jìn)一步給出了一種基于偏好解聚的多準(zhǔn)則層次分類決策方法,該方法集中了現(xiàn)有PROMETHEE、ELECTRE和M.H.DIS方法的優(yōu)點(diǎn),克服了其中的不足。 對(duì)分類優(yōu)化模型的求解是本文研究的一個(gè)重點(diǎn)和難點(diǎn)。在分析了常規(guī)方法失效而進(jìn)化算法具有求解優(yōu)勢(shì)的情況下,引入進(jìn)化策略,并結(jié)合動(dòng)態(tài)及模擬退火罰函數(shù)構(gòu)造出了一種較好的求解算法,通過計(jì)算機(jī)編程實(shí)現(xiàn),有效地解決了模型計(jì)算難度大、求解時(shí)間長(zhǎng)的問題。然后基于此算法,設(shè)計(jì)并開發(fā)了以上述模型為核心的交互式多準(zhǔn)則分類決策系統(tǒng)。該系統(tǒng)運(yùn)算速度快,可靠性高,并具有一種交互反饋機(jī)制,使決策者可以監(jiān)控中間過程,修正結(jié)果,逐步降低人的主觀性影響,從而快速獲得有效的分類結(jié)果。 最后通過算例對(duì)比分析和驗(yàn)證了此方法的有效性和科學(xué)性,為其它相關(guān)領(lǐng)域和學(xué)科中的類似決策問題提供了一種有益參考。
[Abstract]:Multi-criteria classification decision making is an important branch of multi-criteria decision making. It is a common problem in management decision making and social life. It deals with the problem of how to classify multiple schemes under multiple criteria. In practical decision making, the preference value of the scheme is not necessarily a linear combination of the preferences of each criterion, that is, the criteria are not completely independent, but are related to each other. At the same time, it is difficult for decision-makers to determine all preference functions and weight coefficients in advance, and usually can only give the corresponding incomplete information. Therefore, a systematic study is carried out on the multi-criteria decision making problem in nonlinear cases with this kind of incomplete information. Based on a large number of references, this paper first discusses the current situation of research on multi-criteria decision making and classification methods at home and abroad, and analyzes the limitations or shortcomings of the existing methods, such as MAUTTECTRER / UTADISM / UTADISM / H.DIS. The optimization model of multi-criteria classification with nonlinear preference aggregation function is improved and constructed, and a multi-criteria hierarchical classification decision method based on preference clustering is presented by using the idea of preference deconcentration and hierarchical progression. This method focuses on the advantages of the existing Prometheek-ELECTRE and M.H.DIS methods, and overcomes the shortcomings of them. It is an important and difficult point to solve the classification optimization model in this paper. After analyzing the failure of conventional methods and the advantage of evolutionary algorithm, the evolutionary strategy is introduced. Combined with dynamic and simulated annealing penalty function, a better algorithm is constructed. Through computer programming, the problem of difficult model calculation and long solution time is solved effectively. An interactive multi-criteria classification decision system based on the above model is designed and developed. The system has the advantages of fast operation, high reliability, and an interactive feedback mechanism, which enables the decision makers to monitor the intermediate process and correct the results. Gradually reduce the subjective impact of human, so as to quickly obtain effective classification results. Finally, the validity and scientificalness of the method are verified by comparison and analysis of numerical examples, which provides a useful reference for similar decision making problems in other related fields and disciplines.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2004
【分類號(hào)】：C934

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本文編號(hào)：1558934