Abstract

Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse’s key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)—the GM (1, 1) model—is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model—PSOGM (1, 1)—to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model—GAGM (1, 1)—the discrete grey model—DGM (1, 1)—to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

中文翻译：

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基于新的基于粒子群算法的灰色模型在仓库绩效预测中的应用

摘要

仓库是供应链网络的关键组成部分。对于供应链从业人员和行业经理来说，提高仓库的运营效率和生产率至关重要。总体仓库效率在很大程度上取决于协同绩效。经理们需要预先估算整个仓库的绩效（OWP），这需要对仓库的关键绩效指标（KPI）进行准确的预测。这项研究旨在以较低的预测误差来预测孟加拉国成衣（RMG）仓库的KPI，以便精确地测量OWP。这项研究结合了专家的建议，进行了文献综述并接受了数据可用性的局限性，从而确定了13个KPI。传统的灰色方法（GM）-GM（1，1）模型-建立用来估计具有有限历史信息但不是绝对的灰色数据的模型。为了减少GM（1，1）的局限性，本文介绍了一种基于新型粒子群优化（PSO）的灰色模型-PSOGM（1，1）-以较小的预测误差来预测仓库的KPI。这项研究还使用基于遗传算法（GA）的灰色模型GAGM（1，1）-离散灰色模型DGM（1，1），根据平均绝对百分比误差评估了所提出模型的性能和其他评估指标。通过对5个月内的KPI进行预测，该模型在预测OWP方面优于现有的灰色模型。为了将PSO和GA算法的最佳参数与灰色模型结合起来，本研究采用了Taguchi设计方法。最后，