Online from: 1982
Subject Area: Electrical & Electronic Engineering
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|Title:||Robust balancing of mixed model assembly line|
|Author(s):||Weida Xu, (Department of Automation, Tsinghua University, Beijing, People's Republic of China), Tianyuan Xiao, (Department of Automation, Tsinghua University, Beijing, People's Republic of China)|
|Citation:||Weida Xu, Tianyuan Xiao, (2009) "Robust balancing of mixed model assembly line", COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 28 Iss: 6, pp.1489 - 1502|
|Keywords:||Assembly lines, Multimodel lines, Programming and algorithm theory|
|Article type:||Research paper|
|DOI:||10.1108/03321640910992038 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||The authors wish to thank both Hongbo Sun and Zhenxiao Gao for stimulating discussion and for very useful formulations, several of which are embedded in this paper. The authors are also grateful for the valuable comments and suggestions of Ze Tao and anonymous reviewers that lead to the paper's significant improvement.|
Purpose – The purpose of this paper is to introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes.
Design/methodology/approach – Scenario planning approach is used to represent the input data uncertainty in the decision model. Two kinds of robust criteria are provided: one is min-max related; and the other is
Findings – With min-max related robust criteria, the solutions can provide an optimal worst-case hedge against uncertainties without a significant sacrifice in the long-run performance;
Research limitations/implications – This paper is an attempt to robust mixed model assembly line balancing. Some more efficient and effective robust approaches – including robust criteria and optimization algorithms – may be designed in the future.
Practical implications – In an assembly line with significant uncertainty, the robust approaches proposed in this paper can hedge against the risk of poor system performance in bad scenarios.
Originality/value – Using robust optimization approaches to balance mixed model assembly line.
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