基于联盟博弈?云化TODIM的方案评价方法研究

2020-08-03 07:58张志霞张二双
现代电子技术 2020年8期
关键词:云模型

张志霞 张二双

摘  要: 针对方案评价中评级主体多考虑评价信息的模糊性而忽略信息随机性,在主观判断时常伴有参照依赖和损失规避等特性,提出一种基于云模型和联盟博弈法改进的交互式多属性决策(TODIM)方案评价方法。在方案评价中,选用云模型量化语义评价信息,计算专家评价云与算术平均云兼顾形状?距离的云模型综合相似度以获取专家的动态权重,引入联盟博弈法求取各指标组合权重,采用云模型与联盟博弈法改进的TODIM方法计算每个方案相对于其他方案的综合排序值,实现对方案的优选,最后通过智能识别系统候选方案的择优证明该方法的有效性。

关键词: 方案评價; 联盟博弈; TODIM法; 云模型; 组合权重求取; 方案优选

中图分类号: TN911?34; TH122                 文献标识码: A                       文章编号: 1004?373X(2020)08?0022?07

Research on TODIM scheme evaluation method based on alliance

game and cloudification

ZHANG Zhixia, ZHANG Ershuang

(School of Management, Xian University of Architecture and Technology, Xian 710000, China)

Abstract: In allusion to the problem that the rating bodies lay stress on the fuzziness of evaluation information but ignore the information′s randomness in the scheme evaluation, which is often accompanied by the characteristics of reference dependence and loss avoidance in the subjective judgment, an improved interactive multi?attribute decision?making ([TO] mada de Decisao Interativa Multicritévio, TODIM) scheme evaluation method based on cloud model and alliance game method is proposed. In the scheme evaluation, the cloud model is adopted to quantize semantic evaluation information, and the comprehensive similarity of the cloud model that the specialist evaluation cloud and the arithmetic average cloud give consideration to both shape and distance is calculated to obtain the specialists′ dynamic weights, and get the combination weight of each evaluation index by introducing the alliance game method. The improved TODIM method based on cloud model and alliance game method is used to compute the comprehensive sorting value of each scheme relative to the others, so as to realize the optimization of schemes. The effectiveness of this method is proved by the merit of candidate schemes in the intelligent identification system.

Keywords: scheme evaluation; alliance game; TODIM method; cloud model; combination weight derivation; scheme optimal selection

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