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Task allocation optimization in collaborative customized product development based on double-population adaptive genetic algorithm

delete2014-07-04
delete14
PRE
AI
B
Beifang Bao
杨育 (Yang Yu) *
Q
Qian Chen
A
Aijun Liu
J
Jiali Zhao
DOI:10.1007/s10845-014-0937-0delete
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Abstract

Abstract

En 中文
Task allocation is one of the most important activities in the process of collaborative customized product development. At present, how to allocate the collaborative development tasks scientifically and rationally becomes one of the hot research issues in the field of product development. Although many scholars in academia has made a significant contribution to the problem of task allocation and achieved many useful results, the research work of collaborative development task allocation for product customization is still lacking. Therefore, in view of the insufficient consideration on task fitness and task coordination for task allocation in collaborative customized product development at present, research work in this paper is conducted based on the analysis of collaborative customized product development process and task allocation strategy. The definition and calculation formula of task fitness and task coordination efficiency are given firstly, then the multi-objective optimization model of product customization task allocation is constructed and the solving method based on the model of double-population adaptive genetic algorithm is proposed. Finally, the feasibility and the effectiveness of task allocation algorithm are tested and verified by the example of a 5MW wind turbine product development project.
Keywords:
Collaborative customized product development
Task allocation
Task fitness
Task coordination efficiency
Double-population adaptive genetic algorithm
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Journal

Journal of Intelligent Manufacturing cover
Journal of Intelligent Manufacturing
IF:
7.4
Papers:
3.5K
Citations:
1.1W

Organization

C
Chongqing University
Scholars:
5.1W
Papers: 4.1W
Citations: 6.0W
X
Xidian University
Scholars:
2.4W
Papers: 1.9W
Citations: 9.7K