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Complicating Features in Industrial Grouping Problems

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Grouping Genetic Algorithms

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

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Abstract

Grouping problems are generally hard problems that are inundated with a number of complicating features. These features pose challenges to decision makers when modeling and solving the problems. Based on recent case studies in the literature, this chapter identifies common complicating features in grouping problems. These features are classified into model abstraction, the presence of multiple constraints, fuzzy management goals, and computational complexity. An in-depth taxonomic study of the cases revealed four types of the complicating features. The study brings more insights into the general grouping problems and the inadequacies of solution methods applied. Deriving from the study, more suitable approaches are then suggested. The study recommends approaches that favor the use of multi-criteria and flexible and other interactive approaches that make use of techniques such as fuzzy set theory, fuzzy logic, multi-criteria decision making, and expert systems.

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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). Complicating Features in Industrial Grouping Problems. In: Grouping Genetic Algorithms. Studies in Computational Intelligence, vol 666. Springer, Cham. https://doi.org/10.1007/978-3-319-44394-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-44394-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44393-5

  • Online ISBN: 978-3-319-44394-2

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