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一种用于设计概念评估的综合专家体重确定方法。

An integrated expert weight determination method for design concept evaluation.

机构信息

Advanced Manufacturing Technology Center, Shandong University of Science and Technology, Qingdao, 266590, China.

Shandong Jiaotong University, Jinan, 250357, China.

出版信息

Sci Rep. 2022 Apr 15;12(1):6358. doi: 10.1038/s41598-022-10333-6.

DOI:10.1038/s41598-022-10333-6
PMID:35428829
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9012764/
Abstract

Expert weight determination is a critical issue in the design concept evaluation process, especially for complex products. However, this phase is often ignored by most decision makers. For the evaluation of complex product design concepts, experts are selected by clusters with different backgrounds. This work proposes a novel integrated two-layer method to determine expert weight under these circumstances. In the first layer, a hybrid model integrated by the entropy weight model and the Multiplicative analytical hierarchy process method is presented. In the second layer, a minimized variance model is applied to reach a consensus. Then the final expert weight is determined by the results of both layers. A real-life example of cruise ship cabin design evaluation is implemented to demonstrate the proposed expert weight determination method. To analyze the feasibility of the proposed method, weight determination with and without using experts is compared. The result shows the expert weight determination method is an effective approach to improve the accuracy of design concept evaluation.

摘要

专家权重确定是设计概念评估过程中的一个关键问题,特别是对于复杂产品。然而,这一阶段往往被大多数决策者所忽视。对于复杂产品设计概念的评估,专家是通过具有不同背景的群组来选择的。针对这种情况,本工作提出了一种新颖的集成双层方法来确定专家权重。在第一层中,提出了一种由熵权模型和乘法层次分析法集成的混合模型。在第二层中,应用最小方差模型来达成共识。然后,通过两层的结果来确定最终的专家权重。通过对客船舱室设计评估的实际案例进行了实施,以验证所提出的专家权重确定方法。为了分析所提出方法的可行性,比较了使用和不使用专家进行权重确定的结果。结果表明,专家权重确定方法是提高设计概念评估准确性的有效方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/1ab6d11b4e27/41598_2022_10333_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/8e0c15039479/41598_2022_10333_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/e00a33de0efc/41598_2022_10333_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/673315fc0af7/41598_2022_10333_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/8a1ed42f16d4/41598_2022_10333_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/d98ee5f9eb06/41598_2022_10333_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/b9251cfe489e/41598_2022_10333_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/f8639fd35b7d/41598_2022_10333_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/1ab6d11b4e27/41598_2022_10333_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/8e0c15039479/41598_2022_10333_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/e00a33de0efc/41598_2022_10333_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/673315fc0af7/41598_2022_10333_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/8a1ed42f16d4/41598_2022_10333_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/d98ee5f9eb06/41598_2022_10333_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/b9251cfe489e/41598_2022_10333_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/f8639fd35b7d/41598_2022_10333_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b78c/9012764/1ab6d11b4e27/41598_2022_10333_Fig8_HTML.jpg

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