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<p>Inventory optimization is a critical challenge in the garment manufacturing industry, where material usage fluctuates significantly across production cycles. Controlling inventory will boost firm profit and satisfy unexpected usage. This study proposes an optimal order quantity model that addresses usage variability scenarios by leveraging a genetic algorithm (GA) approach. The model aims to minimize total inventory costs by optimizing order quantities under multiple usage fluctuation scenarios, ensuring efficient resource allocation while meeting production requirements. A case study at a garment accessory manufacturer demonstrates the effectiveness of the proposed model in reducing average inventory costs by 30.74% compared to the company’s existing ordering policy. The findings highlight the potential of GA-based optimization in improving inventory management, particularly in industries with high usage uncertainty.</p>

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An Optimal Order Quantity Model for Usage Variability Scenarios in the Garment Industry Using Genetic Algorithm Approach

  • 期刊论文

  • 发表日期：

    2025年8月30日

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  • Le Duc Dao,
  • Kieu Minh Hieu

摘要

Inventory optimization is a critical challenge in the garment manufacturing industry, where material usage fluctuates significantly across production cycles. Controlling inventory will boost firm profit and satisfy unexpected usage. This study proposes an optimal order quantity model that addresses usage variability scenarios by leveraging a genetic algorithm (GA) approach. The model aims to minimize total inventory costs by optimizing order quantities under multiple usage fluctuation scenarios, ensuring efficient resource allocation while meeting production requirements. A case study at a garment accessory manufacturer demonstrates the effectiveness of the proposed model in reducing average inventory costs by 30.74% compared to the company’s existing ordering policy. The findings highlight the potential of GA-based optimization in improving inventory management, particularly in industries with high usage uncertainty.