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Towards the efficient generation of variant design in product development networks: network nodes importance based product configuration evaluation approach

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第2期34卷 615-631页

作者： Guo, Yuming Jinggangshan Univ Coll Mech & Elect Engn Jian 343009 Jiangxi Peoples R China

The variant design node configuration is an effective method to solve a trade-off that exist in mass customization production between the requirements of product diversification and the product's cost and delivery time. However, configuration changes in a product development network may generate an avalanche effect of design change propagation, which can consume substantial design resources. In order to control design change propagations, an evaluation method based on network nodes importance for variant design node schemes is proposed for a product development network. Firstly, the evaluation indexes, including the betweenness, variant deign node set importance, and network clustering coefficient, are integrated to describe the network characteristics of the set of variant design nodes. Then, combining the time and resource constraints for the variant design, a discrete particle swarm algorithm is employed to optimize the configuration of the nodes. The configuration solution for the variant design nodes satisfies the need to control the variant design propagation. Compared with the established greedy algorithm, the discrete particle swarm optimization can achieve better optimization performance in terms of the algorithm's convergence and computation time. It is meaningful to understand the mechanism of product configuration change propagation in depth in order to choose the variant design nodes rationally and efficiently in complex product development networks. Lastly, an example of variant design for a type of cleaning robot product verifies the effectiveness of the proposed method.

关键词： Product development network Variant design Nodes configuration Nodes importance network clustering coefficient Discrete particle swarm algorithm

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