Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number

Grey target decision model for mixed attributes including real numbers, interval numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers is complex for its data processing in different ways and information distortion in handling fuzzy numbers. To solve these problems, the binary connection...

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Main Authors: Jinshan Ma, Changsheng Ji
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/763543
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spelling doaj-3cb8fe4e76974bb4a14503c5aa420ec02020-11-24T23:32:05ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/763543763543Generalized Grey Target Decision Method for Mixed Attributes Based on Connection NumberJinshan Ma0Changsheng Ji1School of Mines, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou, Jiangsu 221116, ChinaGrey target decision model for mixed attributes including real numbers, interval numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers is complex for its data processing in different ways and information distortion in handling fuzzy numbers. To solve these problems, the binary connection number proposed in set pair analysis is applied to unify different types of index values with their parameters’ average values and standard deviations as determinacy-uncertainty vectors. Then the target center index vectors are determined by the modules of index vectors of all alternatives under different attributes. So the similarity of each index vector and its target center index vector called nearness degree can be calculated. Following, all the nearness degrees are normalized in linear method in order to be compared with each other. Finally, the optimal alternative can be determined by the minimum of all integrated nearness degrees. Case study demonstrated that this approach can not only unify different types of numbers, and simplify the calculation but also reduce the information distortion in operating fuzzy numbers.http://dx.doi.org/10.1155/2014/763543
collection DOAJ
language English
format Article
sources DOAJ
author Jinshan Ma
Changsheng Ji
spellingShingle Jinshan Ma
Changsheng Ji
Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number
Journal of Applied Mathematics
author_facet Jinshan Ma
Changsheng Ji
author_sort Jinshan Ma
title Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number
title_short Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number
title_full Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number
title_fullStr Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number
title_full_unstemmed Generalized Grey Target Decision Method for Mixed Attributes Based on Connection Number
title_sort generalized grey target decision method for mixed attributes based on connection number
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2014-01-01
description Grey target decision model for mixed attributes including real numbers, interval numbers, triangular fuzzy numbers, and trapezoidal fuzzy numbers is complex for its data processing in different ways and information distortion in handling fuzzy numbers. To solve these problems, the binary connection number proposed in set pair analysis is applied to unify different types of index values with their parameters’ average values and standard deviations as determinacy-uncertainty vectors. Then the target center index vectors are determined by the modules of index vectors of all alternatives under different attributes. So the similarity of each index vector and its target center index vector called nearness degree can be calculated. Following, all the nearness degrees are normalized in linear method in order to be compared with each other. Finally, the optimal alternative can be determined by the minimum of all integrated nearness degrees. Case study demonstrated that this approach can not only unify different types of numbers, and simplify the calculation but also reduce the information distortion in operating fuzzy numbers.
url http://dx.doi.org/10.1155/2014/763543
work_keys_str_mv AT jinshanma generalizedgreytargetdecisionmethodformixedattributesbasedonconnectionnumber
AT changshengji generalizedgreytargetdecisionmethodformixedattributesbasedonconnectionnumber
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