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Assembly Line Balancing

A Hybrid Grouping Genetic Algorithm Approach

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Grouping Genetic Algorithms

Part of the book series: Studies in Computational Intelligence ((SCI,volume 666))

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Abstract

Assembly line balancing is a hard problem concerned with the assignment of individual work elements or tasks to workstations so as to minimize the assembly cost as much as possible. Decisions associated with line balancing have a direct influence on the costs of production. Basic heuristic algorithms have been widely used to solve the assembly line balancing problem (ALBP). However, to solve hard, large-scale ALBPs, efficient decision tools are essential. Therefore, developing efficient algorithms that can yield optimal or near-optimal solutions is of utmost importance. In this chapter, a hybrid grouping genetic algorithm is developed to address complex problems. The algorithm incorporates basic heuristics, unique genetic operators, and other techniques to enhance the optimization search process. Comparative computational results based on established test problems are presented. The results show that the hybrid grouping genetic algorithm is more efficient and effective than competitive algorithms. The algorithm can be used to assist decision makers in assembly line balancing decisions, with minimal computational requirements.

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Authors and Affiliations

  1. Faculty of Engineering, Namibia University of Science and Technology, Windhoek, Namibia

    Michael Mutingi

  2. Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Michael Mutingi & Charles Mbohwa

Authors
  1. Michael Mutingi
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  2. Charles Mbohwa
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Correspondence to Michael Mutingi .

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Mutingi, M., Mbohwa, C. (2017). Assembly Line Balancing. In: Grouping Genetic Algorithms. Studies in Computational Intelligence, vol 666. Springer, Cham. https://doi.org/10.1007/978-3-319-44394-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-44394-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44393-5

  • Online ISBN: 978-3-319-44394-2

  • eBook Packages: EngineeringEngineering (R0)

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