【正文】 responding documents. The document [5] believed that the integrated judgment matrix contributed by weighted arithmetic average method cannot extend the reciprocity of original judgment matrix, therefore, there is no consistency exists, but it will show clear randomness that arithmetic average method is used partially to maintain reciprocity, and then the new judgment matrix is constructed on a reciprocal basis. The document [6] by utilizing specific example also proved that the additive convex bination is not valid as well，however, it also believes that by using the simple geometric average, the generated matrices convert all the consistent and inconsistent information from expert judgments to a plete consistent positive reciprocal matrix, and also claim that it would be more reasonable to use weighted geometric average to get adjusted matrix by adopting weighted coefficients, which are generated on principle of consistency and rule of minority yield. Thus, this paper believed that the aggregation of judgment matrixes should be mutual proved and selected in variety of schemes to choose more consistent results. For aggregation of expert matrix A1，A2，A3，A4，A5, are more reasonable to be selected as the weights of index system G= (G1, G2, G3, G4, G5, G6, G7).5 ConclusionThe calculation results of weights and indexes sorting getting from different schemes of matrix aggregation differ from each other, how to reduce this difference effectively and reach more reasonable results requires to adopt multiple aggregation methods in group decision making process, then selection and optimization must be done on the calculation results produced by those methods. This paper believes that the application of multiple methods and optimization and selection in practice will benefit in improving rationality and consistency of matrix aggregation in group decision making process.Reference1. Lv Yuejin, Guo Xinrong. An Effective Aggregation Method for Group AHP Judgment Matrix. Theory and Practice of Systems Engineering, 2007(7):132136.2. Liu Xin, Yang Shanlin. Hadamard Convex Combinations of Judgment Matrix. Theory and Practice of Systems Engineering, 2000,20(4):8385.3. Yang Shanlin, Liu Xinbao. Two Aggregation Method of Judgment Matrix in GDSS. Journal of Computers, 2001, 24(1):106111.4. Yang Shanlin, Liu Xinbao. Research on optimizing principle of Convex Combination coefficients of Judgment Matrix. Theory and Practice of Systems Engineering, 2001,21(8):5052.5. Xu Zeshui. A note in Document [1] and [2] for the Properties of Convex Combinations of Judgment Matrix. Theory and practice of Systems Engineering, 2001,21(1):139140.6. Wang Jian, Huang Fenggang, Jin Shaoguang. Study on Adjustment Method for Consistency of Judgment Matrix in AHP. Theory and Practice of Systems Engineering, 2005(8):8591.18。 Aggregation。相反本文認(rèn)為, 實(shí)際中有各樣的困難存在于判斷矩陣的重建。步驟5：通過(guò)加法或乘法的方法匯總各組，并記錄結(jié)果為。領(lǐng)域中的矩陣聚合,李躍進(jìn)和郭欣榮利用連通的無(wú)向圖及其理論,通過(guò)排除偏見(jiàn)的專(zhuān)家判斷, 從理論的面向電力圖、簡(jiǎn)單的mth無(wú)向連通圖想出了一個(gè)互反判斷矩陣聚合方法。它通常涉及多個(gè)決策者,因此,多個(gè)判斷矩陣提供不同的決策者需要匯總,以便達(dá)到更合理的解決方案。詳細(xì)的步驟和解釋見(jiàn)文檔步驟1:建立一致性專(zhuān)家判斷矩陣；步驟2: 在決策過(guò)程中設(shè)置品位偏差矩陣代表專(zhuān)家在年代價(jià)值觀重要性排名比較指標(biāo)i和j (2)步驟3：選擇（n1）元素，這是有級(jí)偏差的最小值，同時(shí)，要求任一項(xiàng)的第（n1）元素還沒(méi)有由其他第（n2）元素給出；步驟4：從專(zhuān)家判斷矩陣中，選擇在 相同的位置的所有元素，并記錄為。原始文檔在專(zhuān)家矩陣無(wú)法達(dá)到一致性時(shí)，要求專(zhuān)家判斷矩陣重建。 Judgment Matrix