中性多属性决策的智能算法解决方案及其在椅子家具舒适度设计评价中的应用

IF 0.6 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Knowledge-Based and Intelligent Engineering Systems Pub Date : 2023-09-14 DOI:10.3233/kes-230123
Le Wang, Jian Ding
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引用次数: 0

摘要

公共场所的活动日益频繁,公共座椅成为公共空间的重要组成部分,成为人们工作、生活、休闲的“辅助工具”。中国对这类家具的需求正在逐渐增加,但公共座椅的设计研究工作却落后于其他民用和办公家具的发展现状和速度。通常,公共座椅根据其用途可分为两部分:公共室内座椅和公共室外座椅。如何认识公共座椅对室内环境的作用和影响,如何在整体环境系统的背景下建立新的公共座椅设计方法,是当前急需解决的问题。椅子家具舒适性设计评价是一个经典的MADM问题。本文构造了具有单值嗜中性集的MADM的广义加权Bonferroni均值算子(WBM)和幂平均算子(PA)。在此基础上,建立了广义单值中性数幂WBM (GSVNNPWBM)算子,并提出了基于GSVNNPWBM算子的MADM决策方法。最后,以座椅家具舒适性设计评价为例,进行了对比分析,验证了GSVNNPWBM方法的有效性。
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Smart algorithmic solutions for neutrosophic multiple-attribute decision-making and applications to chair furniture comfort design evaluation
The activities in public places are becoming increasingly frequent, and public seats have become an important component of public space and an “auxiliary tool” for work, life, and leisure. The demand for such furniture in China is gradually increasing, but the design and research work of public seats is lagging behind the development status and speed of other civil and office furniture. Usually, public seats can be divided into two parts based on their usage: public indoor seats and public outdoor seats. How to understand the role and impact of public seats on the indoor environment, and how to establish a new design method for public seats in the context of the overall environmental system, are currently urgent issues to be solved. The chair furniture comfort design evaluation is a classical MADM issues. In such paper, the generalized weighted Bonferroni mean (WBM) operator and power average (PA) is constructed for MADM with single-valued neutrosophic sets (SVNSs). Then, the generalized single-valued neutrosophic number power WBM (GSVNNPWBM) operator is built and then the MADM decision methods are proposed based on the GSVNNPWBM operator. Finally, an example about chair furniture comfort design evaluation and some comparative analysis were given to demonstrate the GSVNNPWBM method.
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发文量
22
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