An improved optimization method for materials distribution based on spatiotemporal clustering in automobile assembly lines

Sheng Qu, Yaoguang Hu*, Lixiang Zhang, Shan Lu

*此作品的通讯作者

科研成果: 期刊稿件会议文章同行评审

5 引用 (Scopus)

摘要

The development of smart factories has put forward more flexible logistics needs for automobile assembly system, and efficient scheduling strategies to meet these requirements still demand prompt solution. Thus, this paper focuses on the problem of materials distribution with automated guided vehicles (AGVs) in automobile assembly lines. The mathematical model is established in the light of actual situation with mixed time windows and an improved genetic algorithm (GA) is developed. Considering the demand characteristics both in time and space, material demand points are clustered based on their spatiotemporal distance to generate the initial population. Then, selection, crossover and mutation operators of GA are also ameliorated as necessary to minimize the total travel cost. Finally, practical examples are carried out to demonstrate the effectiveness of this methodology.

源语言英语
页(从-至)241-246
页数6
期刊Procedia CIRP
97
DOI
出版状态已出版 - 2020
活动8th CIRP Conference of Assembly Technology and Systems, CATS 2020 - Athens, 希腊
期限: 29 9月 20201 10月 2020

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