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Optimizing goods retrieval becomes crucial as large-scale logistics demand for automated storage systems grows. This paper presents a novel approach using Genetic Algorithms (GA) to cluster clients by port in large-scale AutoStore systems. By grouping client orders based on port proximity, we aim to reduce retrieval times and improve system throughput. Our GA-based method allocates tasks to Automated Guided Vehicles (AGVs), optimizing travel distance and operational efficiency within a three-dimensional grid layout. Simulation results show significant improvements in retrieval efficiency, demonstrating the potential of GA-based clustering to enhance performance in large-scale warehouse operations like AutoStore.

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Clustering Clients by Port in Large-Scale AutoStore Using Genetic Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Won Yong Ha

摘要

Optimizing goods retrieval becomes crucial as large-scale logistics demand for automated storage systems grows. This paper presents a novel approach using Genetic Algorithms (GA) to cluster clients by port in large-scale AutoStore systems. By grouping client orders based on port proximity, we aim to reduce retrieval times and improve system throughput. Our GA-based method allocates tasks to Automated Guided Vehicles (AGVs), optimizing travel distance and operational efficiency within a three-dimensional grid layout. Simulation results show significant improvements in retrieval efficiency, demonstrating the potential of GA-based clustering to enhance performance in large-scale warehouse operations like AutoStore.