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基于熵权迭代的区域物流多属性模糊群决策评估模型

2021-05-05赵晶英

物流科技 2021年9期

赵晶英

摘  要:为了快速、便捷地对区域物流水平进行评估,提出一种多属性群决策评估模型。设立自然语意变量集,建立自然语意变量与梯形模糊数的对应关系,构建包含顶层指标、3个中间层指标、12个底层指标的三级评估指标体系。基于群体熵最小化原则,通过循环修正计算得到用于指标重要性判断的专家权重和对评估区域进行评定的专家权重,通过确定底层指标、中间层指标的相对指标权重,依次计算评估对象在底层指标、中间层指标、顶层指标的评定值,并根据评估对象在顶层指标的评定值确定评估对象的可能度矩阵及排序向量,最后基于排序值对评估对象的区域物流水平进行排序,通过评估还有助于找出评估对象的相对优势及不足。算例研究验证了评估模型的可行性和有效性。

关键词:模糊判断;自然语意变量;梯形模糊数;群体熵;排序向量

中图分类号:F272    文献标识码:A

Abstract: In order to evaluate the regional logistics level quickly and conveniently, a multi-attribute group decision evaluation model is designed. A set of natural semantic variables is established, and the corresponding relationship between natural semantic variables and trapezoidal fuzzy numbers is instituted. A three-level evaluation index system is constructed including top-level indicator, three intermediate-level indicators, and 12 bottom-level indicators. Based on the principle of population entropy minimizing, the expert weights of expert judging indicators' importance and the expert weights of experts' assessment on evaluation objects are obtained through cyclic modificatory calculation. The relative index weights of the bottom-level indicators and the intermediate-level indicators are calculated, and the assessment values of the evaluation objects in the bottom-level indicators, the intermediate-level indicators, and the top-level indicator are calculated in turn. According to the assessment value of the evaluation object in the top-level indicator, the likelihood matrix and the sorting vector of the evaluation object are obtained, and the regional logistics level of the evaluation object is sorted based on the sorting value. The assessment also helps to identify the relative strengths and weaknesses of the evaluation objects. The case study verifies the feasibility and effectiveness of the evaluation model.

Key words: fuzzy judgment; natural semantic variables; trapezoidal fuzzy number; population entropy; sorting vector

對区域物流的发展水平进行评估,可以获得有关评估对象区域物流方面的关键信息,为大型物流企业的区域布局、网点选址等决策提供可靠依据。进行初步评估时,快速和便捷是关键,而对评估结果的精度要求相对较低。用白化指标进行评估,通常其数据来源更客观,评估结果更可靠,但是评估周期长、评估成本高;相反,用灰色指标进行评估,其信息来源较主观,但是其评估过程快速、便捷。

对区域物流水平进行评估时,一些文献[1-3]建立的指标体系较简单,导致评估模型的可拓展性不强,不利于其他评估目的的研究所参考,比如当其他学者使用更多层次的评估指标体系时,原评估模型难以进行利用。……

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