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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (12): 2298-2306    DOI: 10.3785/j.issn.1008-973X.2021.12.009
    
Construction of industrial design decision-making network and its dynamic evolution simulation
Yan-pu YANG(),Zheng GONG,Chen-xin LAN,Zi-jing LEI,Xin-rui WANG
School of Construction Machinery, Chang’an University, Xi’an 710064, China
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Abstract  

Aiming at the problem of opinion evolution in multi-stage decision-making in the industrial design process, the complex network theory was integrated to establish an industrial design decision-making network model, and the dynamic evolution of decision-making opinions was simulated through opinion dynamics. A weighted undirected network of design decision-making was established based on graph theory. The weight of decision-makers was ascertained based on changes of the network efficiency, and the trust set of design decision-making network nodes was identified with the help of opinion distance. The weighted average opinions were utilized to drive the update and evolution of the design decision-making network. The evolutionary simulation process of industrial design decision-making network was proposed, and the dynamic simulation analysis was implemented with decision-making data of product design schemes. Results show that the trust threshold value determines the topology of design decision-making network, and opinion differences among decision-making individuals are gradually reduced and a consensus is finally reached with the network evolving. The design decision network can help extract noise nodes and their opinions changes, which should be gained more attention in actual product design decision-making. The opinion evolution can assist in determining the decision-making round of industrial design schemes, clarifying the performance of product scheme in various indicators and identifying the direction of design improvement. The simulation analysis will help discover the key factors and the evolution law of opinions in design decision-making.

Key words： industrial design      design decision-making      complex network      opinion dynamics      evolution simulation     
Received: 09 February 2021      Published: 31 December 2021
CLC:  TB 472  
Fund:  国家自然科学基金资助项目(51805043)；中央高校基金资助项目(300102259202)；中国博士后基金资助项目(2019M663604)；陕西省创新能力支撑计划资助项目(2020PT-014)
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Yan-pu YANG
Zheng GONG
Chen-xin LAN
Zi-jing LEI
Xin-rui WANG
Cite this article:

Yan-pu YANG,Zheng GONG,Chen-xin LAN,Zi-jing LEI,Xin-rui WANG. Construction of industrial design decision-making network and its dynamic evolution simulation. Journal of ZheJiang University (Engineering Science), 2021, 55(12): 2298-2306.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.12.009     OR     https://www.zjujournals.com/eng/Y2021/V55/I12/2298


工业设计决策网络构建及其动态演化仿真

针对工业设计过程多阶段方案决策的意见演化问题，融合复杂网络理论建立工业设计决策网络模型，通过意见动力学进行决策意见动态演化仿真.基于图论建立设计决策有权无向网络，以网络效率变化确定决策者权重，借助意见距离识别设计决策网络节点信任集合，利用加权平均意见驱动设计决策网络更新与演化. 提出工业设计决策网络的演化仿真流程，结合产品设计方案决策数据进行动态仿真分析，结果表明：信任阈值决定设计决策网络拓扑构成，随着网络演化的进行，决策个体间意见差异逐步缩小并最终达成共识；设计决策网络分析能够析出噪声节点及其意见变化，在实际产品设计决策中应重点关注；意见演化能够辅助确定工业设计方案决策轮次，明确方案在各指标上的表现并识别设计改进方向；仿真分析有助于发现设计决策中的关键因素和意见演化规律.


关键词： 工业设计,  设计决策,  复杂网络,  意见动力学,  演化仿真 
vi ${x} ^*_{ij}$
I1 I2 I3 I4 I5 I6 I7 I8 I9
1 9 6 7 8 3 7 6 8 9
2 5 8 3 8 6 7 7 2 5
3 8 9 6 9 8 6 2 7 8
4 8 6 6 2 7 3 8 8 8
5 9 8 6 7 3 6 8 6 9
6 10 7 8 9 6 9 9 7 10
7 8 8 7 6 7 6 8 7 8
8 6 4 7 3 5 6 9 4 6
9 10 7 7 9 6 9 8 6 10
Tab.1 Decision-making data of product design scheme
Fig.1 Initial decision-making network of product design
Fig.2 Degree distribution of initial decision-making network
Fig.3 Evolved decision-making network of product design
Fig.4 Trust threshold value change
Fig.5 Decision-maker’s opinion evolution of nine indicators
Fig.6 Opinion evolution of nine decision-makers
t $ w_i^t $
v1 v2 v3 v4 v5 v6 v7 v8 v9
1 0.168 7 0.000 0 0.026 4 0.070 1 0.165 2 0.112 8 0.309 1 0.032 8 0.114 5
2 0.117 4 0.076 1 0.106 3 0.047 3 0.158 2 0.109 1 0.189 8 0.057 9 0.137 5
3 0.107 1 0.008 1 0.121 1 0.075 9 0.144 6 0.121 0 0.145 1 0.139 3 0.137 3
4 0.092 4 0.069 5 0.068 7 0.075 2 0.131 5 0.139 6 0.163 7 0.145 2 0.113 7
5 0.138 3 0.015 4 0.086 2 0.112 9 0.140 8 0.148 4 0.111 8 0.113 1 0.132 6
6 0.121 2 0.025 9 0.085 6 0.110 3 0.121 2 0.129 3 0.149 6 0.117 6 0.139 0
7 0.133 2 0.054 2 0.101 2 0.105 6 0.104 2 0.118 4 0.130 2 0.142 8 0.109 7
8 0.127 6 0.011 8 0.121 7 0.091 0 0.089 7 0.151 2 0.127 6 0.151 3 0.127 6
Tab.2 Weight change of network nodes
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