【正文】 許多決策方面。 畢業(yè)設(shè)計 （外文翻譯）畢 業(yè) 設(shè) 計（論文）題 目： Application of Matrix Aggregation Method in Group Decision Making Process學(xué) 院： 數(shù)理學(xué)院 專業(yè)名稱： 信息與計算科學(xué) 學(xué) 號： 200941210104 學(xué)生姓名： 石夢弟 指導(dǎo)教師： 明廷橋 2013年2月20日矩陣聚合方法在群體決策過程的應(yīng)用周威廉（合肥科技大學(xué)經(jīng)濟管理學(xué)院，安徽合肥）摘要：在群體決策過程中，不同的矩陣集合計劃將產(chǎn)生各種不同的排名關(guān)系和權(quán)值向量。在分析和應(yīng)用兩種凸組合的阿達瑪基于矩陣聚合方案及圖論之后,本文將從不同矩陣聚合的判斷中探索更合理的方法來測驗,選擇和優(yōu)化結(jié)果。它通常涉及多個決策者,因此,多個判斷矩陣提供不同的決策者需要匯總,以便達到更合理的解決方案。然而,劉欣和楊善麗基于判斷矩陣發(fā)展了阿達瑪凸組合,提供了關(guān)于 “添加法”和“乘法”凸組合一致性明顯改善的證據(jù)。同時,在這個過程中矩陣的聚合,同一聚合方案也存在不同的判斷矩陣不一致地聚合。本文將探討矩陣的可行性和存在的問題,從聚合方案啟動圖論和阿達瑪凸組合,做出相關(guān)的驗證、優(yōu)化和選擇。詳細的步驟和解釋見文檔步驟1:建立一致性專家判斷矩陣；步驟2: 在決策過程中設(shè)置品位偏差矩陣代表專家在年代價值觀重要性排名比較指標i和j (2)步驟3：選擇（n1）元素，這是有級偏差的最小值，同時，要求任一項的第（n1）元素還沒有由其他第（n2）元素給出；步驟4：從專家判斷矩陣中，選擇在 相同的位置的所有元素，并記錄為。步驟6：在（N1）中使用加法合成得到，并建立綜合判斷矩陣A *，應(yīng)用該方法的總結(jié)，計劃最終排序。如果A1，A2，..... Am在數(shù)量為m前提下是判斷矩陣，相同的問題，假如存在使得 （3） （4）因此，被命名為A1，A2，…Am的一個額外的凸組合，是一個阿達瑪乘法凸組合。B則在此基礎(chǔ)上,文檔[3] 解釋了“加法”和“乘法”凸組合的判斷矩陣的基本理論,并認為“加法”和“乘法”凸組合判斷矩陣不僅可以消除主觀因素的影響,也可以保持和提高判斷矩陣的一致性,同時證明了相應(yīng)過程,因此它證實“加法”和“乘法”凸組合判斷矩陣在群體決策支持系統(tǒng)中對判斷矩陣是兩個有效的聚合方法。原始文檔在專家矩陣無法達到一致性時，要求專家判斷矩陣重建。該報稱,專家數(shù)據(jù)不一致或不太一致可以忽略和簡化問題,專家判斷矩陣由評價指標體系的重要性G =(GGGG4,G5,G6,G7)判定,條件是它是符合一致性,符合一致的比率CR由小到大排序,排在前五位的專家判斷矩陣如下: 經(jīng)計算得一致性比率：步驟2:特級偏差矩陣的建立，如上；步驟3:選擇根據(jù)特級偏差矩陣E,選擇更高級別一致性的6個元素并且得到無向連通圖。無向連通圖(F1)基于特級偏差矩陣E 因為它是不符合要求的無向連通圖,v1 v2 v4 v5,v1 v2 v3都在形成回路。省略細節(jié)流程,得到無向圖的連接圖2和圖3。 Judgment Matrix。 Optimization1 IntroductionAs an effective method utilized in multiobjective and multifactor decision making, Analytic Hierarchy Process has been widely applied in many decision making aspects. It normally involves several decision makers, therefore, multiple judgment matrixes provided by different decision maker need to be aggregated so that to reach a more reasonable solution. In the field of matrix aggregation, Lv Yuejin and Guo Xinrong utilized the Connected Undirected Graph and its theories, by excluding the biased expert judgments, have e up with a reciprocal judgment matrix aggregation method which was oriented from the theory of mth power graph of simple undirected connected graph [1]. Nevertheless,Liu Xin and Yang Shanlin developed Hadamard convex bination based on judgment matrix [23], provided the evidence on “additive”and“multiplicative” convex binations consistency improvement as well. The document [4] has studied on the optimization principle related with the convex coefficients of judgment matrix, and provided solution to the convex bination coefficients of judgment matrix.Different matrix aggregation schemes will process the expert judgment data with discrepancy and aggregation results of judgment matrix are produced differently, therefore the weight and consistency after calculation are differential from another. While, in the process of matrix aggregation, the same aggregation schemes also present discrepantly in different judgment matrix aggregation. In the practice of solving problems, it is necessary to adopt different matrix aggregation methods and implement relevant verification and choice. This paper will investigate the feasibility and problems of matrix aggregation schemes that are initiated from graph theory and Hadamard convex binations, andrelevant verification, optimizing and choice have been made.