Abstract
Kullback-Leibler Distance Based Generalized Grey Target Decision Method With Index and Weight Both Containing Mixed Attribute Values
Highlights
The decision-making basis (DMB) of grey target decision method (GTDM) is referred to as the target center distance (TCD), which is the distance of each alternative and its target center
The difficulty of generalized grey target decision making with index and weight both containing mixed attribute values lies in [12]:(1) different types of weight values cannot be aggregated directly with mixed index values;(2) the simple conversion of different types of data into deterministic weights without scientific and reasonable method may lead to large deviation and affect the accuracy of decision;(3) how to address the mixed index weights in a unified, simple and accurate way
The CWP based grey target decision method (GGTDM) with index and weight both containing mixed attribute values has two ways to fulfill the decision-making task: one way is to obtain the certain weights by proximity-based method and adopt the CWP as the DMB [12]; the other is to arrive at the certain weights by module-based method and take the CWP as the DMB [21]
Summary
The decision-making basis (DMB) of grey target decision method (GTDM) is referred to as the target center distance (TCD), which is the distance of each alternative and its target center. In certain number-based GTDM, Euclidean distance and Mahalanobis distance are often applied to obtain the TCDs [1], [2]. The mixed attribute-based GTDM obtains the TCD in different ways: at first, the conventional Euclidean distance-based method was reported [3]–[5]. J. Ma et al.: Kullback-Leibler Distance Based GGTDM With Index and Weight Both Containing Mixed Attribute Values meaning of distance [19]. The GGTDM with index and weight both containing mixed attribute values has been investigated. This work proposes a K-L distance-based method involving mixed attribute weight values. The final decision making is based on the value of comprehensive weighted K-L distance with which the smaller the better
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