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基于鲁棒性的链路权重规划算法 被引量:1
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作者 罗宇 吕光宏 《计算机与现代化》 2014年第1期71-76,共6页
在IP网络中,链路权重规划是流量工程中的重要问题。为了优化网络流量并实现负载均衡,针对业务量矩阵的不确定性,依据鲁棒性理论提出一个链路权重规划的MIP模型。该模型使用Γ模型描述业务量矩阵的不确定集,通过表示扰动程度的参数Γ实... 在IP网络中,链路权重规划是流量工程中的重要问题。为了优化网络流量并实现负载均衡,针对业务量矩阵的不确定性,依据鲁棒性理论提出一个链路权重规划的MIP模型。该模型使用Γ模型描述业务量矩阵的不确定集,通过表示扰动程度的参数Γ实现了对鲁棒性的调节,在此基础上求得不确定集中最差情况下的最优解。实验结果表明,与传统的链路权重规划方法以及新出现的MRC规划模型相比较,该算法可降低最大链路利用率,分别达到了40%和20%。 展开更多
关键词 不确定流量矩阵 鲁棒性 权重规划 MIP模型 F模型
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APPROACH FOR FUZZY MULTI-ATTRIBUTE DECISION-MAKING WITH FUZZY COMPLEMENTARY PREFERENCE RELATION ON ALTERNATIVES 被引量:2
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作者 周宏安 刘三阳 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2007年第1期74-79,共6页
In presented fuzzy multi-attribute decision-making (FMADM) problems, the information about attribute weights is interval numbers and the decision maker (DM) has fuzzy complementary preference relation on alternati... In presented fuzzy multi-attribute decision-making (FMADM) problems, the information about attribute weights is interval numbers and the decision maker (DM) has fuzzy complementary preference relation on alternatives. Firstly, the decision-making information based on the subjective preference information in the form of the fuzzy complementary judgment matrix is uniform by using a translation function. Then an objective programming model is established. Attribute weights are obtained by solving the model, thus the fuzzy overall values of alternatives are derived by using the additive weighting method. Secondly, the ranking approach of alternatives is proposed based on the degree of similarity between the fuzzy positive ideal solution of alternatives (FPISA) and the fuzzy overall values. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible. It is easy to be operated and implemented on a computer. Finally, the proposed method is applied to the project evaluation in the venture investment. 展开更多
关键词 fuzzy multi-attribute decision-making objective programming WEIGHT similarity degree PRIORITY
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