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Neural Computing and Applications

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01.07.2014 | Original Article

Bacterial Foraging Optimization Algorithm for assembly line balancing

verfasst von: Yakup Atasagun, Yakup Kara

Erschienen in: Neural Computing and Applications | Ausgabe 1/2014

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Abstract

Assembly line balancing is the problem of assigning tasks to workstations by optimizing a performance measure while satisfying precedence relations between tasks and cycle time restrictions. Many exact, heuristic and metaheuristic approaches have been proposed for solving simple straight and U-shaped assembly line balancing problems. In this study, a relatively new optimization algorithm, Bacterial Foraging Optimization Algorithm (BFOA), based heuristic approach is proposed for solving simple straight and U-shaped assembly line balancing problems. The performance of the proposed algorithm is evaluated using a well-known data set taken from the literature in which the number of tasks varies between 7 and 111, and results are also compared with both an ant-colony-optimization-based heuristic approach and a genetic-algorithm-based heuristic approach. The proposed algorithm provided optimal solutions for 123 out of 128 (96.1 %) test problems in seconds and is proven to be promising.

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Titel: Bacterial Foraging Optimization Algorithm for assembly line balancing
verfasst von: Yakup Atasagun
Yakup Kara
Publikationsdatum: 01.07.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1477-9

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