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https://doi.org/10.1007/978-1-4039-1370-8spective (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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Fuzzy Grouping Genetic Algorithms: Advances for Real-World Grouping Problemsand to handle complex real-world grouping problems with fuzzy characteristics. It is hoped that the proposed fuzzy GGA (FGGA) presented in this chapter is an effective and efficient algorithm for solving real-world grouping problems, even in a fuzzy environment. Avenues for further research in this

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Optimizing Order Batching in Order Picking Systems: Hybrid Grouping Genetic Algorithmtimes of the HGGA are generally shorter when compared to other algorithms. Thus, reduced length of picker tours leads to the overall reduction of the order picking time, which cuts down on overtime, workforce size, and the overall operational costs, while improving the quality of service.

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Assembly Line Balancinglts 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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