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Grouping Genetic Algorithms pp 213–228Cite as

Modeling Supplier Selection Using Multi-Criterion Fuzzy Grouping Genetic Algorithm

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

Abstract

Supplier evaluation and selection are one of the most important activities in various industry disciplines. However, evaluating suppliers in a fuzzy and multi-criterion environment is a complex responsibility. In some cases, the tasks to be done by the suppliers have due dates and precedence constraints, for example, in construction subcontractor selection. In most industry settings, this activity involves conflicting management goals, multiple criteria, and constraints. Price, lead time, quality, and number of suppliers or vendors selected are some of the conflicting criteria that have to be optimized simultaneously. Such situations demand advanced efficient, flexible, and interactive decision support systems that can handle fuzzy variable. This chapter presents a fuzzy multi-criterion modeling approach for handling supplier selection problems from a fuzzy grouping genetic algorithm perspective (FGGA). The multi-criterion FGGA uses fuzzy evaluation methods to model multiple criteria by converting management goals and aspirations into normalized fuzzy membership functions. Illustrations are provided based on typical examples such as subcontractor selection.

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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). Modeling Supplier Selection Using Multi-Criterion Fuzzy Grouping Genetic Algorithm. In: Grouping Genetic Algorithms. Studies in Computational Intelligence, vol 666. Springer, Cham. https://doi.org/10.1007/978-3-319-44394-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-44394-2_12

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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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