An iterative decomposition for asynchronous mixed-model assembly lines: combining balancing, sequencing, and buffer allocation

Jan 1, 2020·
Thiago Cantos Lopes
Celso Gustavo Stall Sikora
Celso Gustavo Stall Sikora
,
Adalberto Sato Michels
,
Leandro Magatão
· 0 min read
Abstract
Asynchronous Mixed-Model Assembly lines are common production layouts dedicated to large-scale manufacturing of similar products. Cyclically scheduling such products is an interesting strategy to obtain high and stable throughput. In order to best optimise these lines, it is necessary to combine line balancing, model sequencing, and buffer allocation. However, few works integrate these three degrees of freedom, and evaluating steady-state performance as a consequence of these decisions is challenging. This paper presents a mathematical model that allows an exact steady-state performance evaluation of these lines, and hence their optimisation. While the combination of degrees of freedom is advantageous, it is also computational costly. An iterative decomposition procedure based on alternation between two mathematical models and on optimality cuts is also presented. The decomposition is tested against the proposed mathematical model in a 700-instance dataset. The developed methods obtained 142 optimal answers. Results show that the decomposition outperforms the monolithic mathematical model, in particular for larger and harder instances in terms of solution quality. The optimality cuts are also shown to help the decomposition steps in terms of solution quality and time. Comparisons to a sequential procedure further demonstrate the importance of simultaneously optimising the three degrees of freedom, as both the proposed model and the decomposition outperformed such procedure.
Type
Publication
International Journal of Production Research
publications
Celso Gustavo Stall Sikora
Authors
Postdoctoral researcher
Postdoctoral researcher in Operations Research at Friedrich-Schiller-Universität Jena. Specialist in exact optimization methods, decomposition approaches, and AI-enhanced combinatorial optimization, with applications in urban logistics, port operations, and assembly systems.