The Linear Combination Weights Method Based on Maximum Entropy Principle
The Linear Combination Weights Method Based on Maximum Entropy Principle
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发表时间：
2011
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通讯作者：
Y. Li-jun
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作者：
Y. Li-jun
考虑到备选方案实际值与理想值之间的广义距离之和最小，提出了一种更合理地分配不同加权方法组合系数信息的模型。随着广义距离之和逐渐变小，通过不同加权方法得到组合权重，计算出备选方案的估值并给出备选方案的排名。论文的贡献如下：首先，组合权重能够反映最可靠的信息分布和最小广义距离。其次，提出了一种用单目标优化模型计算多目标规划Pareto解集的新方法，并根据解集给出了备选方案的排序，从而使评估结果更加可靠，同时为多目标模型的求解提供了新的思路。再次，组合系数不等于递减的总和，更加合理。第四，消除了目标间组合系数的不确定性。
Considering the minimum sum of generalized distance between the actual values and ideal values of the alternatives,this paper presents a model for distributing the information of the combination coefficients of different weighting methods more reasonably.With the sum of generalized distance becoming smaller gradually,the combination weights are obtained by different weighting methods.The valuations of alternatives are calculated and the rank of alternatives is given.The contribution of the paper is as follows.Firstly,the combination weights can reflect the most reliable information distribution and the minimum generalized distance sum.Secondly,a new method is presented to calculate the Pareto solution set of multi-objectives programming by a single objective optimization model.The rank of alternatives is given according to the solution set.Therefore,the result of the valuation is more reliable.At the same time,a new idea is provided for solving multi-objectives model.Thirdly,the combination coefficients are not equal with the decreasing sum,which are more reasonable.Fourthly,the uncertainty of the combination coefficients between the objectives is eliminated.