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一些常用的多属性决策方法在处理多属性评价问题时,易出现评价结果相近,评价值差异较小、区分度不高等问题.运用离差最大化思想构建的多属性决策模型,能有效拉大评价值间的差异,便于决策方案的优选和排序.但该模型在实践中,只考虑了因属性值差异性引起的可变权重,忽略了评价指标本身的属性权重,易造成评价结果偏离真实结果.鉴于此,对已有的基于离差最大化的多属性决策模型进行改进,综合考虑评价属性的可变权重和属性权重,设计了改进的离差最大化决策模型,并将该模型应用于临近空间多任务系统综合效能评估中,为临近空间多任务规划方案的排序及优选提供决策支持.最后,通过算例,对比验证了该模型具有一定的适用性.
Some common multi-attribute decision-making methods are prone to problems such as similar evaluation results, small differences in evaluation value and poor discrimination when dealing with multi-attribute evaluation problems.Multi-attribute decision-making model constructed by the maximization of deviation maximizes the effectiveness of multi- However, the model only considers the variable weight caused by the difference of attribute value in practice, ignores the attribute weight of the evaluation index itself and easily leads to the deviation of the evaluation result from the reality In view of this, we improve the existing multi-attribute decision-making model based on the maximization of deviation, and consider the variable weight and the attribute weight of the evaluation attributes, and design an improved dispersion maximum decision-making model, and apply the model In the evaluation of the comprehensive performance of multi-tasking systems in near space, the model provides the decision support for the ranking and optimization of multitask programs in the near space.Finally, an example is given to verify the applicability of the model.