Scholarlink

<p>When developing logistics networks, old-fashioned solutions usually can’t keep up with the complicated and dynamic real-world data. This makes things not work exactly correctly and costs more to conduct business. This study looks at how <i>Deep Learning Algorithms</i> (DLA) could improve logistics networks by finding data patterns and generating predictions based on those patterns. To be more specific, it looks at how different tactics may help with planning. These include prior logistical data, transportation costs, demand forecasts, warehouse locations, and a deep neural network (DNN) model. The deep neural network (DNN) learns from a dataset with real-world operational data and simulated logistical situations. The next stage examines how these findings compare to more traditional optimization methods. Important results show that the new deep learning framework is better than the best at predicting the best network topologies. The technology also makes great strides in lowering costs, making service levels more consistent, and making routes more efficient. Researchers are presently trying to improve recurrent neural networks (RNNs) to handle better time-based changes and convolutional neural networks (CNNs) to handle space-based changes better. Deep learning performs better than classic heuristic and linear programming approaches when the input is unexpected. Lastly, employing deep learning to develop logistics networks could be a smart solution to remedy the issues with current supply chains that can be used on a big scale. This study has proved that AI can improve logistics, and now this work can look forward to making smarter, more automated choices based on data. The research looks beyond DNN-based planning to evaluate the varying performance of different DNN architectures (e.g., CNNs for spatial data and RNNs for sequential demand patterns) depending on the characteristics of the dataset. The models’ responses to varying data distributions (e.g., seasonal demand fluctuations, variations in transport costs, and heterogeneous warehouse capacity data) were mapped out to assess model robustness. The ensuing analysis indicates that while no architecture has shown to dominate universally, hybrid and ensemble techniques can be utilized to yield consistent improvements across datasets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application of Deep Learning Algorithms in Logistics Network Layout Planning

  • 期刊论文

  • 发表日期：

    2026年4月11日

查看全文
  • Jin Wang

摘要

When developing logistics networks, old-fashioned solutions usually can’t keep up with the complicated and dynamic real-world data. This makes things not work exactly correctly and costs more to conduct business. This study looks at how Deep Learning Algorithms (DLA) could improve logistics networks by finding data patterns and generating predictions based on those patterns. To be more specific, it looks at how different tactics may help with planning. These include prior logistical data, transportation costs, demand forecasts, warehouse locations, and a deep neural network (DNN) model. The deep neural network (DNN) learns from a dataset with real-world operational data and simulated logistical situations. The next stage examines how these findings compare to more traditional optimization methods. Important results show that the new deep learning framework is better than the best at predicting the best network topologies. The technology also makes great strides in lowering costs, making service levels more consistent, and making routes more efficient. Researchers are presently trying to improve recurrent neural networks (RNNs) to handle better time-based changes and convolutional neural networks (CNNs) to handle space-based changes better. Deep learning performs better than classic heuristic and linear programming approaches when the input is unexpected. Lastly, employing deep learning to develop logistics networks could be a smart solution to remedy the issues with current supply chains that can be used on a big scale. This study has proved that AI can improve logistics, and now this work can look forward to making smarter, more automated choices based on data. The research looks beyond DNN-based planning to evaluate the varying performance of different DNN architectures (e.g., CNNs for spatial data and RNNs for sequential demand patterns) depending on the characteristics of the dataset. The models’ responses to varying data distributions (e.g., seasonal demand fluctuations, variations in transport costs, and heterogeneous warehouse capacity data) were mapped out to assess model robustness. The ensuing analysis indicates that while no architecture has shown to dominate universally, hybrid and ensemble techniques can be utilized to yield consistent improvements across datasets.