Title
A Novel Hierarchical Collaborative Method Based on Multi-objective Optimization for Modularization of Product Platform
Abstract
The modular design of product platform becomes an indispensable aspect in manufacturing industry with increasingly tailored requirements from customers. Amongst all components in this platform, the modularization mechanism addresses key issue in the design process of manufacturing product. However, the traditional modular methods only solve the modularization with single one or several objectives. In this paper, a novel hierarchical collaborative method based on multi-objective optimization is developed for modularization of product platform, which is integrated various of modular standards, including the product structure, product function, design of products, assembly of products and so on, and trades off the contradiction of these standards to achieve the optimal modularization with multiple hierarchical objectives. Moreover, for solutions to this method, a hierarchical collaborative multi-objective optimization algorithm, the Hierarchical Non-dominated Sorting Genetic Algorithm (H-NSGA), is developed to optimize the modularization with multilevel objectives. Furthermore, an industrial case illustrates the details of the proposed model and algorithm. Finally, the experimental comparison demonstrates that the hierarchical collaborative method and the hierarchical multi-objective optimization algorithm can achieve efficient modular solutions with multiple hierarchical objectives.
Year
DOI
Venue
2019
10.1109/CSCWD.2019.8791901
2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Keywords
DocType
ISBN
product platform,modularization,hierarchical collaborative method,Hierarchical Non-dominated Sorting Genetic Algorithm (H-NSGA)
Conference
978-1-7281-0351-8
Citations 
PageRank 
References 
0
0.34
3
Authors
6
Name
Order
Citations
PageRank
Qihao Wan101.01
Jiaxin Zhao201.69
J. Xue354257.57
Chiyuan Zhang485531.65
Bin He5852110.09
Heming Zhang69728.48