Task Decomposition and Grouping for Customer Collaboration in Product Development

• Main Authors: Zhang Xuefeng, Yang Yu, Bao Beifang
• Format: Article
• Language: English
• Published: De Gruyter 2016-07-01
• Series: Journal of Intelligent Systems
• Subjects: product development; customer collaboration; task decomposition; task grouping; design structure matrix; adaptive genetic algorithm; 90b50
• Online Access: https://doi.org/10.1515/jisys-2014-0171

Decomposing product development project into various tasks and grouping them are important activities in product development. Many scholars devoted their efforts to solving this problem and proposed some useful methods. However, the research work of task decomposition and grouping for customer collaboration in product development is still lacking. Therefore, this study first decomposes product development tasks and analyzes its executability. Then, by using an integrated numerical design structure matrix and adaptive genetic algorithm (AGA) approach, tasks are divided into different groups, tasks in the same group have high correlation degree, and tasks in the different groups have low correlation degree. To illustrate the process of task decomposition and grouping methods proposed in this paper, a mobile phone structural development case is applied. Moreover, standard generic algorithm (SGA) and particle swarm optimization (PSO) are used to compare with AGA to verify the effectiveness of AGA.