仓储是现代物流中降低供应链成本的重要环节。针对传统方法难以有效评估仓储操作效率的问题,提出了一种基于仿真技术的优化方法。该方法通过构建仓储仿真模型,模拟不同作业策略的效果,并重点优化模型参数设置。研究利用历史数据提取关...仓储是现代物流中降低供应链成本的重要环节。针对传统方法难以有效评估仓储操作效率的问题,提出了一种基于仿真技术的优化方法。该方法通过构建仓储仿真模型,模拟不同作业策略的效果,并重点优化模型参数设置。研究利用历史数据提取关键操作效率指标,并结合回归方法预测特定流程的处理时间,从而提供准确的参数输入,提升模型与实际仓储环境的匹配度。实验验证表明,基于回归分析的参数生成方法具有较高的可靠性和实用性,仿真结果与实际情况高度吻合,证明了该模型在优化仓储性能方面的有效性。Warehousing is a critical component of modern logistics, playing a key role in reducing supply chain costs. To address the limitations of traditional methods in effectively evaluating warehouse operation efficiency, an optimization approach based on simulation technology is proposed. This approach involves constructing a warehouse simulation model to simulate the effects of different job strategies and focuses on optimizing model parameter settings. Historical data is used to extract key operational efficiency metrics, combined with regression methods to predict processing times for specific processes, providing accurate parameter inputs and enhancing the model’s alignment with actual warehouse environments. Experimental validation demonstrates that the parameter generation method based on regression analysis is highly reliable and practical. The simulation results align closely with real-world scenarios, confirming the model’s effectiveness in optimizing warehouse performance.展开更多
文摘仓储是现代物流中降低供应链成本的重要环节。针对传统方法难以有效评估仓储操作效率的问题,提出了一种基于仿真技术的优化方法。该方法通过构建仓储仿真模型,模拟不同作业策略的效果,并重点优化模型参数设置。研究利用历史数据提取关键操作效率指标,并结合回归方法预测特定流程的处理时间,从而提供准确的参数输入,提升模型与实际仓储环境的匹配度。实验验证表明,基于回归分析的参数生成方法具有较高的可靠性和实用性,仿真结果与实际情况高度吻合,证明了该模型在优化仓储性能方面的有效性。Warehousing is a critical component of modern logistics, playing a key role in reducing supply chain costs. To address the limitations of traditional methods in effectively evaluating warehouse operation efficiency, an optimization approach based on simulation technology is proposed. This approach involves constructing a warehouse simulation model to simulate the effects of different job strategies and focuses on optimizing model parameter settings. Historical data is used to extract key operational efficiency metrics, combined with regression methods to predict processing times for specific processes, providing accurate parameter inputs and enhancing the model’s alignment with actual warehouse environments. Experimental validation demonstrates that the parameter generation method based on regression analysis is highly reliable and practical. The simulation results align closely with real-world scenarios, confirming the model’s effectiveness in optimizing warehouse performance.
